Modelos Supervisados

Tarea # 2 Richard Douglas Grijalba

  1. Información General Ventures Capital Inc., para optimizar su cartera de otorgamiento de créditos a empresas que optan por ser financiadas y ellas lo que hacen es proveerle sus estados financieros junto con una columna llamada “Bankrupt”, que define como 0 en caso de que la empresa se reporta en los estados financieros como competente, es decir, que no va a quedar en banca rota, mientras que un 1 significa que la empresa es probable que caiga en banca rota

Qué se requiere analizar en una empresa para determinar la salud o liquidez financiera?

Según la información brindada se puede inferir si una empresa puede o no caer en bancarrota?

In [1347]:
# Importacion de bibliotecas

import numpy as np
import pandas as pd
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
from matplotlib import style
import seaborn as sns
from sklearn.mixture import GaussianMixture #GMM
from sklearn.pipeline import make_pipeline 
from sklearn.preprocessing import StandardScaler
import warnings
warnings.filterwarnings('ignore')
import argparse
from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from kneed import KneeLocator
import plotly.graph_objects as go 
from plotly.subplots import make_subplots
sns.set()
In [ ]:
pip install Kneed
Collecting Kneed
  Downloading kneed-0.7.0-py2.py3-none-any.whl (9.4 kB)
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Installing collected packages: Kneed
Successfully installed Kneed-0.7.0
  1. Importacion del Dataset
In [ ]:
data = pd.read_csv('/content/drive/MyDrive/Data Sets/data 3.csv')
In [ ]:
data.head(5)   # corresponde a un datset de información financiera, en la que presenta información relevante (ratios financieros) 
               # que permiten determinar el nivel de liquidez financiera.
Out[ ]:
Bankrupt? ROA(C) before interest and depreciation before interest ROA(A) before interest and % after tax ROA(B) before interest and depreciation after tax Operating Gross Margin Realized Sales Gross Margin Operating Profit Rate Pre-tax net Interest Rate After-tax net Interest Rate Non-industry income and expenditure/revenue Continuous interest rate (after tax) Operating Expense Rate Research and development expense rate Cash flow rate Interest-bearing debt interest rate Tax rate (A) Net Value Per Share (B) Net Value Per Share (A) Net Value Per Share (C) Persistent EPS in the Last Four Seasons Cash Flow Per Share Revenue Per Share (Yuan ¥) Operating Profit Per Share (Yuan ¥) Per Share Net profit before tax (Yuan ¥) Realized Sales Gross Profit Growth Rate Operating Profit Growth Rate After-tax Net Profit Growth Rate Regular Net Profit Growth Rate Continuous Net Profit Growth Rate Total Asset Growth Rate Net Value Growth Rate Total Asset Return Growth Rate Ratio Cash Reinvestment % Current Ratio Quick Ratio Interest Expense Ratio Total debt/Total net worth Debt ratio % Net worth/Assets Long-term fund suitability ratio (A) ... Current Assets/Total Assets Cash/Total Assets Quick Assets/Current Liability Cash/Current Liability Current Liability to Assets Operating Funds to Liability Inventory/Working Capital Inventory/Current Liability Current Liabilities/Liability Working Capital/Equity Current Liabilities/Equity Long-term Liability to Current Assets Retained Earnings to Total Assets Total income/Total expense Total expense/Assets Current Asset Turnover Rate Quick Asset Turnover Rate Working capitcal Turnover Rate Cash Turnover Rate Cash Flow to Sales Fixed Assets to Assets Current Liability to Liability Current Liability to Equity Equity to Long-term Liability Cash Flow to Total Assets Cash Flow to Liability CFO to Assets Cash Flow to Equity Current Liability to Current Assets Liability-Assets Flag Net Income to Total Assets Total assets to GNP price No-credit Interval Gross Profit to Sales Net Income to Stockholder's Equity Liability to Equity Degree of Financial Leverage (DFL) Interest Coverage Ratio (Interest expense to EBIT) Net Income Flag Equity to Liability
0 1 0.370594 0.424389 0.405750 0.601457 0.601457 0.998969 0.796887 0.808809 0.302646 0.780985 1.256969e-04 0.0 0.458143 0.000725 0.0 0.147950 0.147950 0.147950 0.169141 0.311664 0.017560 0.095921 0.138736 0.022102 0.848195 0.688979 0.688979 0.217535 4.980000e+09 0.000327 0.263100 0.363725 0.002259 0.001208 0.629951 0.021266 0.207576 0.792424 0.005024 ... 0.190643 0.004094 0.001997 1.473360e-04 0.147308 0.334015 0.276920 0.001036 0.676269 0.721275 0.339077 0.025592 0.903225 0.002022 0.064856 7.010000e+08 6.550000e+09 0.593831 4.580000e+08 0.671568 0.424206 0.676269 0.339077 0.126549 0.637555 0.458609 0.520382 0.312905 0.118250 0 0.716845 0.009219 0.622879 0.601453 0.827890 0.290202 0.026601 0.564050 1 0.016469
1 1 0.464291 0.538214 0.516730 0.610235 0.610235 0.998946 0.797380 0.809301 0.303556 0.781506 2.897851e-04 0.0 0.461867 0.000647 0.0 0.182251 0.182251 0.182251 0.208944 0.318137 0.021144 0.093722 0.169918 0.022080 0.848088 0.689693 0.689702 0.217620 6.110000e+09 0.000443 0.264516 0.376709 0.006016 0.004039 0.635172 0.012502 0.171176 0.828824 0.005059 ... 0.182419 0.014948 0.004136 1.383910e-03 0.056963 0.341106 0.289642 0.005210 0.308589 0.731975 0.329740 0.023947 0.931065 0.002226 0.025516 1.065198e-04 7.700000e+09 0.593916 2.490000e+09 0.671570 0.468828 0.308589 0.329740 0.120916 0.641100 0.459001 0.567101 0.314163 0.047775 0 0.795297 0.008323 0.623652 0.610237 0.839969 0.283846 0.264577 0.570175 1 0.020794
2 1 0.426071 0.499019 0.472295 0.601450 0.601364 0.998857 0.796403 0.808388 0.302035 0.780284 2.361297e-04 25500000.0 0.458521 0.000790 0.0 0.177911 0.177911 0.193713 0.180581 0.307102 0.005944 0.092338 0.142803 0.022760 0.848094 0.689463 0.689470 0.217601 7.280000e+09 0.000396 0.264184 0.368913 0.011543 0.005348 0.629631 0.021248 0.207516 0.792484 0.005100 ... 0.602806 0.000991 0.006302 5.340000e+09 0.098162 0.336731 0.277456 0.013879 0.446027 0.742729 0.334777 0.003715 0.909903 0.002060 0.021387 1.791094e-03 1.022676e-03 0.594502 7.610000e+08 0.671571 0.276179 0.446027 0.334777 0.117922 0.642765 0.459254 0.538491 0.314515 0.025346 0 0.774670 0.040003 0.623841 0.601449 0.836774 0.290189 0.026555 0.563706 1 0.016474
3 1 0.399844 0.451265 0.457733 0.583541 0.583541 0.998700 0.796967 0.808966 0.303350 0.781241 1.078888e-04 0.0 0.465705 0.000449 0.0 0.154187 0.154187 0.154187 0.193722 0.321674 0.014368 0.077762 0.148603 0.022046 0.848005 0.689110 0.689110 0.217568 4.880000e+09 0.000382 0.263371 0.384077 0.004194 0.002896 0.630228 0.009572 0.151465 0.848535 0.005047 ... 0.225815 0.018851 0.002961 1.010646e-03 0.098715 0.348716 0.276580 0.003540 0.615848 0.729825 0.331509 0.022165 0.906902 0.001831 0.024161 8.140000e+09 6.050000e+09 0.593889 2.030000e+09 0.671519 0.559144 0.615848 0.331509 0.120760 0.579039 0.448518 0.604105 0.302382 0.067250 0 0.739555 0.003252 0.622929 0.583538 0.834697 0.281721 0.026697 0.564663 1 0.023982
4 1 0.465022 0.538432 0.522298 0.598783 0.598783 0.998973 0.797366 0.809304 0.303475 0.781550 7.890000e+09 0.0 0.462746 0.000686 0.0 0.167502 0.167502 0.167502 0.212537 0.319162 0.029690 0.096898 0.168412 0.022096 0.848258 0.689697 0.689697 0.217626 5.510000e+09 0.000439 0.265218 0.379690 0.006022 0.003727 0.636055 0.005150 0.106509 0.893491 0.005303 ... 0.358380 0.014161 0.004275 6.804636e-04 0.110195 0.344639 0.287913 0.004869 0.975007 0.732000 0.330726 0.000000 0.913850 0.002224 0.026385 6.680000e+09 5.050000e+09 0.593915 8.240000e+08 0.671563 0.309555 0.975007 0.330726 0.110933 0.622374 0.454411 0.578469 0.311567 0.047725 0 0.795016 0.003878 0.623521 0.598782 0.839973 0.278514 0.024752 0.575617 1 0.035490

5 rows × 96 columns

3. Exploración Basica del DataSet

3.1 Descripción de las Variables

Definiciones o Conceptos Financieros Básicos

Algunas variables pueden parecer la misma o muy similares, sin embargo corresponde a un mismo ratio financiero que se le modifica alguna variable en el proceso de su cálculo, por ejemplo la prueba de liquidez en el activo corriente, y posterior a eso existe la misma prueba pero restandole el valor de los inventarios (prueba acida).

Los Ratios Financieros (razones financieras) pueden clasificarse según la finalidad de la información a valor, Ratios de Liquidez, Gestión, Endeudamiento o Ratios de Rentabilidad, la impportancia o utilidad de los ratios financieros recae en que por medio de un valor o factor, permite evaluar un aspecto de la operación,gestión o inversión de una compañía, sin necesidad de ller un informe extenson, una vez que la persona observa los ratios, amplía en información sobre aquellos que le llamó la atención. En lo que respecta a otros indicadores, las personas tienden a confundir flujo de caja con Utilidad, siendo cosas muy distintas, el Flujo de Caja es el "dinero" disponible que la compañia tiene para hacer frente a las actividades de operacion , ciclo de negocio, inversión y financiero. La Compañaía puede obtener recursos de fuentes propias o de terceros (flujo de efectivo). Mientras que la Utilidad (resultado del periodo) se relaciona al resultado de tomar todos los ingresos de la empresa y restarle los costos y gatos. Si el Resultado final es positivo es Utilidad y si es negativo es Pérdida. El flujo de efectivo se ve reflejado en el Estado de Situación Financiera y en el Estado de Flujo de Efectico y la Utilidad se ve generada en el Estado de Resultados (P&L). Los Activos puede ser Fijos (depreciables) La depreciación es el registro de gasto -mensual- por desgaste o escudo fiscal de los activos, los cuales se deprecian por varios métodos. O pueden ser Activos Corrientes (cuentas por cobrar, Inventario, Efectivo) Los Inventarios corresponden a la mercadería que una empresa vende para generar recursos. Intereses corresponden al costo financiero de utilizar financiamiento por medio de recursos de terceros.

Explicado lo anterior se procede a detallar las caracteristicas que intervienen en el Dataset: Se detallan algunas (en el apartado del EDA, se detallan cada na), de igual forma se aportan los links para la consulta detallada sobre ratios e indicadores financieros

Bankrupt?: Aparece como una caracteristica binaria, en donde si indica cero 0, nos dice que la empresa es competente, mientras que si el valor es 1, nos indica que es probable que la empresa caiga en banca rota.

ROA(C) before interest and depreciation before interest, ROA: El rendimiento de los activos es un índice de rentabilidad que proporciona la cantidad de ganancias que una empresa puede generar a partir de sus activos, antes de los gastos de Intereses, Depreciacion.

ROA(A) before interest and % after tax . El rendimiento de los activos es un índice de rentabilidad que proporciona la cantidad de ganancias que una empresa puede generar a partir de sus activos, antes de intereses pero despues de impuestos.

ROA(B) before interest and depreciation after tax, El rendimiento de los activos es un índice de rentabilidad que proporciona la cantidad de ganancias que una empresa puede generar a partir de sus activos , pero en este caso antes de intereses y depreciacion pero despues de impuestos.

Operating Gross Margin: representa el porcentaje de los ingresos totales que le queda a una empresa por encima de los costos directamente relacionados con la producción y la distribución.

Cash flow rate: la tasa de crecimiento a largo plazo del efectivo operativo, el dinero que realmente ingresa a las cuentas de la compañía producto de las operaciones comerciales, la IRR corresponde es una tasa de descuento que hace que el valor actual neto (VAN) de todos los flujos de efectivo sea igual a cero en un análisis de flujo de efectivo descontado.

Quick Ratio: mide la capacidad de una empresa para pagar sus pasivos corrientes sin necesidad de vender su inventario u obtener financiación adicional.

Total Asset Turnover: Mide el peso de las ventas o los ingresos de una empresa en relación con el valor de sus activos. El índice de rotación de activos se puede utilizar como indicador de la eficiencia con la que una empresa utiliza sus activos para generar ingresos.

Net Income to Total Assets: Proporciona la cantidad de beneficios que una empresa puede generar a partir de sus activos.

Total Asset Return Growth Rate Ratio: El rendimiento de los activos totales (ROTA) es una relación que mide las ganancias de una empresa antes de intereses e impuestos (EBIT) en relación con sus activos netos totales

Quick Assets/Current Liability: Es una medida más conservadora de la liquidez de una empresa que los activos corrientes, ya que excluye los inventarios. El índice rápido se utiliza para analizar la capacidad inmediata de una empresa para pagar sus pasivos corrientes sin la necesidad de vender su inventario o utilizar financiación.

Inventory and accounts receivable/Net value: Valor razonable del inventariio y las cuentas por cobrar

Inventory/Current Liability : Proporciona información de la capacidad de la empresa que al realizar ventas de inventario pueda generar el efectivo necesario para cumplir con las obligaciones a corto plazo de los acreedores.

Accounts Receivable Turnover: Esta relación le da a la empresa una idea sólida de la eficiencia con la que recauda las deudas contraídas con el crédito que extendió, y un número menor muestra una mayor eficiencia

Inventory Turnover Rate (times): indica la tasa a la que una empresa vende y reemplaza su stock de bienes durante un período en particular. La fórmula del índice de rotación de inventario es el costo de los bienes vendidos dividido por el inventario promedio para el mismo período

Allocation rate per person:Una tasa de asignación es un porcentaje del efectivo o desembolso de capital de un inversor que se destina a una inversión final.

Total expense/Assets: Es una medida de los costos totales asociados con la administración y operación de un fondo de inversión, como un fondo mutuo. Estos costos consisten principalmente en honorarios de administración y gastos adicionales, como honorarios de negociación, honorarios legales, honorarios de auditor y otros gastos operativos.

Total debt/Total net worth : Es un cálculo simple que puede ayudarlo a evaluar la salud financiera de una empresa determinada al comparar el nivel de deuda que tiene con su patrimonio neto total.

Long-term Liability to Current Assets: Este índice proporciona una medida general de la situación financiera a largo plazo de una empresa, incluida su capacidad para cumplir con sus obligaciones financieras por préstamos pendientes.

Definiciones de ratios o razones financieras

Ratios de Rentabilidad : Return on Assets (ROA), Return on Equity (ROE), Return on Investment (ROI), Return on Invested Capital (ROIC), EBITDA Margin, Net Profit Margin, Operating Margin.

(https://www.investopedia.com/ask/answers/031215/what-formula-calculating-return-assets-roa.asp)

Ratios de Liquidez: Current Ratio, Quick Ratio, Cash Ratio, Operating Cash Flow Ratio, Receivables Turnover Ratio, Inventory Turnover

https://www.investopedia.com/terms/q/quickratio.asp

Ratios de Solvencia: Debt-To-Equity Ratio, Total-Debt-to-Total-Assets Ratio, Interest Coverage Ratio, Shareholder Equity Ratio

https://www.investopedia.com/terms/i/interestcoverageratio.asp

Verificación de datos nulos

In [ ]:
data.isnull().sum()    # según la revisión no parecen existir datos nulo o con problemas
Out[ ]:
Bankrupt?                                                   0
 ROA(C) before interest and depreciation before interest    0
 ROA(A) before interest and % after tax                     0
 ROA(B) before interest and depreciation after tax          0
 Operating Gross Margin                                     0
                                                           ..
 Liability to Equity                                        0
 Degree of Financial Leverage (DFL)                         0
 Interest Coverage Ratio (Interest expense to EBIT)         0
 Net Income Flag                                            0
 Equity to Liability                                        0
Length: 96, dtype: int64
In [ ]:
data.dtypes #  tenemos 96 caracteristicas distintas (columnas)  y 6819 observaciones (filas)
               # todas las caracteristicas presentes son del tipo númerico.  3 del tipo Int  y 93 del tipo Float
Out[ ]:
Bankrupt?                                                     int64
 ROA(C) before interest and depreciation before interest    float64
 ROA(A) before interest and % after tax                     float64
 ROA(B) before interest and depreciation after tax          float64
 Operating Gross Margin                                     float64
                                                             ...   
 Liability to Equity                                        float64
 Degree of Financial Leverage (DFL)                         float64
 Interest Coverage Ratio (Interest expense to EBIT)         float64
 Net Income Flag                                              int64
 Equity to Liability                                        float64
Length: 96, dtype: object
In [ ]:
data.duplicated().sum()  # no presentamos daots duplicados
Out[ ]:
0
In [ ]:
data.size   # tenemos un tamñana de los datos de 654324 datos
Out[ ]:
654624
In [ ]:
data.info()    # tenemos 96 caracteristicas distintas (columnas)  y 6819 observaciones (filas)
               # todas las caracteristicas presentes son del tipo númerico.  3 del tipo Int  y 93 del tipo Float
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6819 entries, 0 to 6818
Data columns (total 96 columns):
 #   Column                                                    Non-Null Count  Dtype  
---  ------                                                    --------------  -----  
 0   Bankrupt?                                                 6819 non-null   int64  
 1    ROA(C) before interest and depreciation before interest  6819 non-null   float64
 2    ROA(A) before interest and % after tax                   6819 non-null   float64
 3    ROA(B) before interest and depreciation after tax        6819 non-null   float64
 4    Operating Gross Margin                                   6819 non-null   float64
 5    Realized Sales Gross Margin                              6819 non-null   float64
 6    Operating Profit Rate                                    6819 non-null   float64
 7    Pre-tax net Interest Rate                                6819 non-null   float64
 8    After-tax net Interest Rate                              6819 non-null   float64
 9    Non-industry income and expenditure/revenue              6819 non-null   float64
 10   Continuous interest rate (after tax)                     6819 non-null   float64
 11   Operating Expense Rate                                   6819 non-null   float64
 12   Research and development expense rate                    6819 non-null   float64
 13   Cash flow rate                                           6819 non-null   float64
 14   Interest-bearing debt interest rate                      6819 non-null   float64
 15   Tax rate (A)                                             6819 non-null   float64
 16   Net Value Per Share (B)                                  6819 non-null   float64
 17   Net Value Per Share (A)                                  6819 non-null   float64
 18   Net Value Per Share (C)                                  6819 non-null   float64
 19   Persistent EPS in the Last Four Seasons                  6819 non-null   float64
 20   Cash Flow Per Share                                      6819 non-null   float64
 21   Revenue Per Share (Yuan ¥)                               6819 non-null   float64
 22   Operating Profit Per Share (Yuan ¥)                      6819 non-null   float64
 23   Per Share Net profit before tax (Yuan ¥)                 6819 non-null   float64
 24   Realized Sales Gross Profit Growth Rate                  6819 non-null   float64
 25   Operating Profit Growth Rate                             6819 non-null   float64
 26   After-tax Net Profit Growth Rate                         6819 non-null   float64
 27   Regular Net Profit Growth Rate                           6819 non-null   float64
 28   Continuous Net Profit Growth Rate                        6819 non-null   float64
 29   Total Asset Growth Rate                                  6819 non-null   float64
 30   Net Value Growth Rate                                    6819 non-null   float64
 31   Total Asset Return Growth Rate Ratio                     6819 non-null   float64
 32   Cash Reinvestment %                                      6819 non-null   float64
 33   Current Ratio                                            6819 non-null   float64
 34   Quick Ratio                                              6819 non-null   float64
 35   Interest Expense Ratio                                   6819 non-null   float64
 36   Total debt/Total net worth                               6819 non-null   float64
 37   Debt ratio %                                             6819 non-null   float64
 38   Net worth/Assets                                         6819 non-null   float64
 39   Long-term fund suitability ratio (A)                     6819 non-null   float64
 40   Borrowing dependency                                     6819 non-null   float64
 41   Contingent liabilities/Net worth                         6819 non-null   float64
 42   Operating profit/Paid-in capital                         6819 non-null   float64
 43   Net profit before tax/Paid-in capital                    6819 non-null   float64
 44   Inventory and accounts receivable/Net value              6819 non-null   float64
 45   Total Asset Turnover                                     6819 non-null   float64
 46   Accounts Receivable Turnover                             6819 non-null   float64
 47   Average Collection Days                                  6819 non-null   float64
 48   Inventory Turnover Rate (times)                          6819 non-null   float64
 49   Fixed Assets Turnover Frequency                          6819 non-null   float64
 50   Net Worth Turnover Rate (times)                          6819 non-null   float64
 51   Revenue per person                                       6819 non-null   float64
 52   Operating profit per person                              6819 non-null   float64
 53   Allocation rate per person                               6819 non-null   float64
 54   Working Capital to Total Assets                          6819 non-null   float64
 55   Quick Assets/Total Assets                                6819 non-null   float64
 56   Current Assets/Total Assets                              6819 non-null   float64
 57   Cash/Total Assets                                        6819 non-null   float64
 58   Quick Assets/Current Liability                           6819 non-null   float64
 59   Cash/Current Liability                                   6819 non-null   float64
 60   Current Liability to Assets                              6819 non-null   float64
 61   Operating Funds to Liability                             6819 non-null   float64
 62   Inventory/Working Capital                                6819 non-null   float64
 63   Inventory/Current Liability                              6819 non-null   float64
 64   Current Liabilities/Liability                            6819 non-null   float64
 65   Working Capital/Equity                                   6819 non-null   float64
 66   Current Liabilities/Equity                               6819 non-null   float64
 67   Long-term Liability to Current Assets                    6819 non-null   float64
 68   Retained Earnings to Total Assets                        6819 non-null   float64
 69   Total income/Total expense                               6819 non-null   float64
 70   Total expense/Assets                                     6819 non-null   float64
 71   Current Asset Turnover Rate                              6819 non-null   float64
 72   Quick Asset Turnover Rate                                6819 non-null   float64
 73   Working capitcal Turnover Rate                           6819 non-null   float64
 74   Cash Turnover Rate                                       6819 non-null   float64
 75   Cash Flow to Sales                                       6819 non-null   float64
 76   Fixed Assets to Assets                                   6819 non-null   float64
 77   Current Liability to Liability                           6819 non-null   float64
 78   Current Liability to Equity                              6819 non-null   float64
 79   Equity to Long-term Liability                            6819 non-null   float64
 80   Cash Flow to Total Assets                                6819 non-null   float64
 81   Cash Flow to Liability                                   6819 non-null   float64
 82   CFO to Assets                                            6819 non-null   float64
 83   Cash Flow to Equity                                      6819 non-null   float64
 84   Current Liability to Current Assets                      6819 non-null   float64
 85   Liability-Assets Flag                                    6819 non-null   int64  
 86   Net Income to Total Assets                               6819 non-null   float64
 87   Total assets to GNP price                                6819 non-null   float64
 88   No-credit Interval                                       6819 non-null   float64
 89   Gross Profit to Sales                                    6819 non-null   float64
 90   Net Income to Stockholder's Equity                       6819 non-null   float64
 91   Liability to Equity                                      6819 non-null   float64
 92   Degree of Financial Leverage (DFL)                       6819 non-null   float64
 93   Interest Coverage Ratio (Interest expense to EBIT)       6819 non-null   float64
 94   Net Income Flag                                          6819 non-null   int64  
 95   Equity to Liability                                      6819 non-null   float64
dtypes: float64(93), int64(3)
memory usage: 5.0 MB
In [ ]:
data.columns
Out[ ]:
Index(['Bankrupt?', ' ROA(C) before interest and depreciation before interest',
       ' ROA(A) before interest and % after tax',
       ' ROA(B) before interest and depreciation after tax',
       ' Operating Gross Margin', ' Realized Sales Gross Margin',
       ' Operating Profit Rate', ' Pre-tax net Interest Rate',
       ' After-tax net Interest Rate',
       ' Non-industry income and expenditure/revenue',
       ' Continuous interest rate (after tax)', ' Operating Expense Rate',
       ' Research and development expense rate', ' Cash flow rate',
       ' Interest-bearing debt interest rate', ' Tax rate (A)',
       ' Net Value Per Share (B)', ' Net Value Per Share (A)',
       ' Net Value Per Share (C)', ' Persistent EPS in the Last Four Seasons',
       ' Cash Flow Per Share', ' Revenue Per Share (Yuan ¥)',
       ' Operating Profit Per Share (Yuan ¥)',
       ' Per Share Net profit before tax (Yuan ¥)',
       ' Realized Sales Gross Profit Growth Rate',
       ' Operating Profit Growth Rate', ' After-tax Net Profit Growth Rate',
       ' Regular Net Profit Growth Rate', ' Continuous Net Profit Growth Rate',
       ' Total Asset Growth Rate', ' Net Value Growth Rate',
       ' Total Asset Return Growth Rate Ratio', ' Cash Reinvestment %',
       ' Current Ratio', ' Quick Ratio', ' Interest Expense Ratio',
       ' Total debt/Total net worth', ' Debt ratio %', ' Net worth/Assets',
       ' Long-term fund suitability ratio (A)', ' Borrowing dependency',
       ' Contingent liabilities/Net worth',
       ' Operating profit/Paid-in capital',
       ' Net profit before tax/Paid-in capital',
       ' Inventory and accounts receivable/Net value', ' Total Asset Turnover',
       ' Accounts Receivable Turnover', ' Average Collection Days',
       ' Inventory Turnover Rate (times)', ' Fixed Assets Turnover Frequency',
       ' Net Worth Turnover Rate (times)', ' Revenue per person',
       ' Operating profit per person', ' Allocation rate per person',
       ' Working Capital to Total Assets', ' Quick Assets/Total Assets',
       ' Current Assets/Total Assets', ' Cash/Total Assets',
       ' Quick Assets/Current Liability', ' Cash/Current Liability',
       ' Current Liability to Assets', ' Operating Funds to Liability',
       ' Inventory/Working Capital', ' Inventory/Current Liability',
       ' Current Liabilities/Liability', ' Working Capital/Equity',
       ' Current Liabilities/Equity', ' Long-term Liability to Current Assets',
       ' Retained Earnings to Total Assets', ' Total income/Total expense',
       ' Total expense/Assets', ' Current Asset Turnover Rate',
       ' Quick Asset Turnover Rate', ' Working capitcal Turnover Rate',
       ' Cash Turnover Rate', ' Cash Flow to Sales', ' Fixed Assets to Assets',
       ' Current Liability to Liability', ' Current Liability to Equity',
       ' Equity to Long-term Liability', ' Cash Flow to Total Assets',
       ' Cash Flow to Liability', ' CFO to Assets', ' Cash Flow to Equity',
       ' Current Liability to Current Assets', ' Liability-Assets Flag',
       ' Net Income to Total Assets', ' Total assets to GNP price',
       ' No-credit Interval', ' Gross Profit to Sales',
       ' Net Income to Stockholder's Equity', ' Liability to Equity',
       ' Degree of Financial Leverage (DFL)',
       ' Interest Coverage Ratio (Interest expense to EBIT)',
       ' Net Income Flag', ' Equity to Liability'],
      dtype='object')
In [ ]:
data1 = data.copy()

Vamos a proceder a quitar un espacio extra que se encuentra en los encabezados

In [ ]:
data1.columns=data1.columns.str.strip()
In [ ]:
data1.head(3)
Out[ ]:
Bankrupt? ROA(C) before interest and depreciation before interest ROA(A) before interest and % after tax ROA(B) before interest and depreciation after tax Operating Gross Margin Realized Sales Gross Margin Operating Profit Rate Pre-tax net Interest Rate After-tax net Interest Rate Non-industry income and expenditure/revenue Continuous interest rate (after tax) Operating Expense Rate Research and development expense rate Cash flow rate Interest-bearing debt interest rate Tax rate (A) Net Value Per Share (B) Net Value Per Share (A) Net Value Per Share (C) Persistent EPS in the Last Four Seasons Cash Flow Per Share Revenue Per Share (Yuan ¥) Operating Profit Per Share (Yuan ¥) Per Share Net profit before tax (Yuan ¥) Realized Sales Gross Profit Growth Rate Operating Profit Growth Rate After-tax Net Profit Growth Rate Regular Net Profit Growth Rate Continuous Net Profit Growth Rate Total Asset Growth Rate Net Value Growth Rate Total Asset Return Growth Rate Ratio Cash Reinvestment % Current Ratio Quick Ratio Interest Expense Ratio Total debt/Total net worth Debt ratio % Net worth/Assets Long-term fund suitability ratio (A) ... Current Assets/Total Assets Cash/Total Assets Quick Assets/Current Liability Cash/Current Liability Current Liability to Assets Operating Funds to Liability Inventory/Working Capital Inventory/Current Liability Current Liabilities/Liability Working Capital/Equity Current Liabilities/Equity Long-term Liability to Current Assets Retained Earnings to Total Assets Total income/Total expense Total expense/Assets Current Asset Turnover Rate Quick Asset Turnover Rate Working capitcal Turnover Rate Cash Turnover Rate Cash Flow to Sales Fixed Assets to Assets Current Liability to Liability Current Liability to Equity Equity to Long-term Liability Cash Flow to Total Assets Cash Flow to Liability CFO to Assets Cash Flow to Equity Current Liability to Current Assets Liability-Assets Flag Net Income to Total Assets Total assets to GNP price No-credit Interval Gross Profit to Sales Net Income to Stockholder's Equity Liability to Equity Degree of Financial Leverage (DFL) Interest Coverage Ratio (Interest expense to EBIT) Net Income Flag Equity to Liability
0 1 0.370594 0.424389 0.405750 0.601457 0.601457 0.998969 0.796887 0.808809 0.302646 0.780985 0.000126 0.0 0.458143 0.000725 0.0 0.147950 0.147950 0.147950 0.169141 0.311664 0.017560 0.095921 0.138736 0.022102 0.848195 0.688979 0.688979 0.217535 4.980000e+09 0.000327 0.263100 0.363725 0.002259 0.001208 0.629951 0.021266 0.207576 0.792424 0.005024 ... 0.190643 0.004094 0.001997 1.473360e-04 0.147308 0.334015 0.276920 0.001036 0.676269 0.721275 0.339077 0.025592 0.903225 0.002022 0.064856 7.010000e+08 6.550000e+09 0.593831 4.580000e+08 0.671568 0.424206 0.676269 0.339077 0.126549 0.637555 0.458609 0.520382 0.312905 0.118250 0 0.716845 0.009219 0.622879 0.601453 0.827890 0.290202 0.026601 0.564050 1 0.016469
1 1 0.464291 0.538214 0.516730 0.610235 0.610235 0.998946 0.797380 0.809301 0.303556 0.781506 0.000290 0.0 0.461867 0.000647 0.0 0.182251 0.182251 0.182251 0.208944 0.318137 0.021144 0.093722 0.169918 0.022080 0.848088 0.689693 0.689702 0.217620 6.110000e+09 0.000443 0.264516 0.376709 0.006016 0.004039 0.635172 0.012502 0.171176 0.828824 0.005059 ... 0.182419 0.014948 0.004136 1.383910e-03 0.056963 0.341106 0.289642 0.005210 0.308589 0.731975 0.329740 0.023947 0.931065 0.002226 0.025516 1.065198e-04 7.700000e+09 0.593916 2.490000e+09 0.671570 0.468828 0.308589 0.329740 0.120916 0.641100 0.459001 0.567101 0.314163 0.047775 0 0.795297 0.008323 0.623652 0.610237 0.839969 0.283846 0.264577 0.570175 1 0.020794
2 1 0.426071 0.499019 0.472295 0.601450 0.601364 0.998857 0.796403 0.808388 0.302035 0.780284 0.000236 25500000.0 0.458521 0.000790 0.0 0.177911 0.177911 0.193713 0.180581 0.307102 0.005944 0.092338 0.142803 0.022760 0.848094 0.689463 0.689470 0.217601 7.280000e+09 0.000396 0.264184 0.368913 0.011543 0.005348 0.629631 0.021248 0.207516 0.792484 0.005100 ... 0.602806 0.000991 0.006302 5.340000e+09 0.098162 0.336731 0.277456 0.013879 0.446027 0.742729 0.334777 0.003715 0.909903 0.002060 0.021387 1.791094e-03 1.022676e-03 0.594502 7.610000e+08 0.671571 0.276179 0.446027 0.334777 0.117922 0.642765 0.459254 0.538491 0.314515 0.025346 0 0.774670 0.040003 0.623841 0.601449 0.836774 0.290189 0.026555 0.563706 1 0.016474

3 rows × 96 columns

In [ ]:
data1.info()  # se limpian los espacios en blanco al inicio de cada nombre de columna
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6819 entries, 0 to 6818
Data columns (total 96 columns):
 #   Column                                                   Non-Null Count  Dtype  
---  ------                                                   --------------  -----  
 0   Bankrupt?                                                6819 non-null   int64  
 1   ROA(C) before interest and depreciation before interest  6819 non-null   float64
 2   ROA(A) before interest and % after tax                   6819 non-null   float64
 3   ROA(B) before interest and depreciation after tax        6819 non-null   float64
 4   Operating Gross Margin                                   6819 non-null   float64
 5   Realized Sales Gross Margin                              6819 non-null   float64
 6   Operating Profit Rate                                    6819 non-null   float64
 7   Pre-tax net Interest Rate                                6819 non-null   float64
 8   After-tax net Interest Rate                              6819 non-null   float64
 9   Non-industry income and expenditure/revenue              6819 non-null   float64
 10  Continuous interest rate (after tax)                     6819 non-null   float64
 11  Operating Expense Rate                                   6819 non-null   float64
 12  Research and development expense rate                    6819 non-null   float64
 13  Cash flow rate                                           6819 non-null   float64
 14  Interest-bearing debt interest rate                      6819 non-null   float64
 15  Tax rate (A)                                             6819 non-null   float64
 16  Net Value Per Share (B)                                  6819 non-null   float64
 17  Net Value Per Share (A)                                  6819 non-null   float64
 18  Net Value Per Share (C)                                  6819 non-null   float64
 19  Persistent EPS in the Last Four Seasons                  6819 non-null   float64
 20  Cash Flow Per Share                                      6819 non-null   float64
 21  Revenue Per Share (Yuan ¥)                               6819 non-null   float64
 22  Operating Profit Per Share (Yuan ¥)                      6819 non-null   float64
 23  Per Share Net profit before tax (Yuan ¥)                 6819 non-null   float64
 24  Realized Sales Gross Profit Growth Rate                  6819 non-null   float64
 25  Operating Profit Growth Rate                             6819 non-null   float64
 26  After-tax Net Profit Growth Rate                         6819 non-null   float64
 27  Regular Net Profit Growth Rate                           6819 non-null   float64
 28  Continuous Net Profit Growth Rate                        6819 non-null   float64
 29  Total Asset Growth Rate                                  6819 non-null   float64
 30  Net Value Growth Rate                                    6819 non-null   float64
 31  Total Asset Return Growth Rate Ratio                     6819 non-null   float64
 32  Cash Reinvestment %                                      6819 non-null   float64
 33  Current Ratio                                            6819 non-null   float64
 34  Quick Ratio                                              6819 non-null   float64
 35  Interest Expense Ratio                                   6819 non-null   float64
 36  Total debt/Total net worth                               6819 non-null   float64
 37  Debt ratio %                                             6819 non-null   float64
 38  Net worth/Assets                                         6819 non-null   float64
 39  Long-term fund suitability ratio (A)                     6819 non-null   float64
 40  Borrowing dependency                                     6819 non-null   float64
 41  Contingent liabilities/Net worth                         6819 non-null   float64
 42  Operating profit/Paid-in capital                         6819 non-null   float64
 43  Net profit before tax/Paid-in capital                    6819 non-null   float64
 44  Inventory and accounts receivable/Net value              6819 non-null   float64
 45  Total Asset Turnover                                     6819 non-null   float64
 46  Accounts Receivable Turnover                             6819 non-null   float64
 47  Average Collection Days                                  6819 non-null   float64
 48  Inventory Turnover Rate (times)                          6819 non-null   float64
 49  Fixed Assets Turnover Frequency                          6819 non-null   float64
 50  Net Worth Turnover Rate (times)                          6819 non-null   float64
 51  Revenue per person                                       6819 non-null   float64
 52  Operating profit per person                              6819 non-null   float64
 53  Allocation rate per person                               6819 non-null   float64
 54  Working Capital to Total Assets                          6819 non-null   float64
 55  Quick Assets/Total Assets                                6819 non-null   float64
 56  Current Assets/Total Assets                              6819 non-null   float64
 57  Cash/Total Assets                                        6819 non-null   float64
 58  Quick Assets/Current Liability                           6819 non-null   float64
 59  Cash/Current Liability                                   6819 non-null   float64
 60  Current Liability to Assets                              6819 non-null   float64
 61  Operating Funds to Liability                             6819 non-null   float64
 62  Inventory/Working Capital                                6819 non-null   float64
 63  Inventory/Current Liability                              6819 non-null   float64
 64  Current Liabilities/Liability                            6819 non-null   float64
 65  Working Capital/Equity                                   6819 non-null   float64
 66  Current Liabilities/Equity                               6819 non-null   float64
 67  Long-term Liability to Current Assets                    6819 non-null   float64
 68  Retained Earnings to Total Assets                        6819 non-null   float64
 69  Total income/Total expense                               6819 non-null   float64
 70  Total expense/Assets                                     6819 non-null   float64
 71  Current Asset Turnover Rate                              6819 non-null   float64
 72  Quick Asset Turnover Rate                                6819 non-null   float64
 73  Working capitcal Turnover Rate                           6819 non-null   float64
 74  Cash Turnover Rate                                       6819 non-null   float64
 75  Cash Flow to Sales                                       6819 non-null   float64
 76  Fixed Assets to Assets                                   6819 non-null   float64
 77  Current Liability to Liability                           6819 non-null   float64
 78  Current Liability to Equity                              6819 non-null   float64
 79  Equity to Long-term Liability                            6819 non-null   float64
 80  Cash Flow to Total Assets                                6819 non-null   float64
 81  Cash Flow to Liability                                   6819 non-null   float64
 82  CFO to Assets                                            6819 non-null   float64
 83  Cash Flow to Equity                                      6819 non-null   float64
 84  Current Liability to Current Assets                      6819 non-null   float64
 85  Liability-Assets Flag                                    6819 non-null   int64  
 86  Net Income to Total Assets                               6819 non-null   float64
 87  Total assets to GNP price                                6819 non-null   float64
 88  No-credit Interval                                       6819 non-null   float64
 89  Gross Profit to Sales                                    6819 non-null   float64
 90  Net Income to Stockholder's Equity                       6819 non-null   float64
 91  Liability to Equity                                      6819 non-null   float64
 92  Degree of Financial Leverage (DFL)                       6819 non-null   float64
 93  Interest Coverage Ratio (Interest expense to EBIT)       6819 non-null   float64
 94  Net Income Flag                                          6819 non-null   int64  
 95  Equity to Liability                                      6819 non-null   float64
dtypes: float64(93), int64(3)
memory usage: 5.0 MB

EDA- ANALISIS EXPLORATORIO DE DATOS

In [ ]:
#De entrada se observan datos atípicos en ciertas caracteristicas, ademas al ser bastantes caracteriticas 
# se deben elegir las que aportan para el proceso de generación de análisis para el estudio del caso
data1[["Bankrupt?","ROA(C) before interest and depreciation before interest","ROA(A) before interest and % after tax","ROA(B) before interest and depreciation after tax", "Operating Gross Margin", "Realized Sales Gross Margin","Operating Profit Rate","Pre-tax net Interest Rate"]].plot(kind='box',subplots=True, layout=(4,4), sharex=False, sharey=False)
sns.set(rc={'figure.figsize':(22,22)})
plt.show()
In [ ]:
#De entrada se observan datos atípicos en ciertas caracteristicas, ademas al ser bastantes caracteriticas 
# se deben elegir las que aportan para el proceso de generación de análisis para el estudio del caso
data1[["After-tax net Interest Rate","Non-industry income and expenditure/revenue","Continuous interest rate (after tax)","Operating Expense Rate", "Research and development expense rate", "Cash flow rate","Interest-bearing debt interest rate","Tax rate (A)"]].plot(kind='box',subplots=True, layout=(4,4), sharex=False, sharey=False)
sns.set(rc={'figure.figsize':(22,22)})
plt.show()

Histogramas en grupo de Caracteristicas Iniciales: Posteriormente se van a generar de forma individual, sin embargo este grupo de permite crear una idea de la distribución de los datos que están presentes

In [ ]:
data1[["Bankrupt?","ROA(C) before interest and depreciation before interest","ROA(A) before interest and % after tax","ROA(B) before interest and depreciation after tax", "Operating Gross Margin", "Realized Sales Gross Margin","Operating Profit Rate","Pre-tax net Interest Rate"]].plot(kind='hist',subplots=True, layout=(4,4), sharex=False, sharey=False)
sns.set(rc={'figure.figsize':(22,22)})
plt.show()
In [ ]:
data1[["After-tax net Interest Rate","Non-industry income and expenditure/revenue","Continuous interest rate (after tax)","Operating Expense Rate", "Research and development expense rate", "Cash flow rate","Interest-bearing debt interest rate","Tax rate (A)"]].plot(kind='hist',subplots=True, layout=(4,4), sharex=False, sharey=False)
sns.set(rc={'figure.figsize':(22,22)})
plt.show()
In [ ]:
datos = data1.copy()   #se realiza una copia del dataset

# vamos a trabajar con la copia datos para el proceso de descripción de las caracteristicas, detalle del EDA, posteriormente hacemos una verificación en correlaciones
# graficas y tablas de detalle.  finalmente proceso de ver el Machine Learning seleccionado, hacemos una corrida del modelo y luego vemos
# los atipicos y haremos una comparativa entre los resultados.  Finalmente una corrida de Modelo No Supervisado, en el cual se utilizarán caracteristicas seleccionadas de interes
# Recordemos que tenemos 96 caracteristicas distintas (columnas)  y 6819 observaciones (filas)
# todas las caracteristicas presentes son del tipo númerico.  3 del tipo Int  y 93 del tipo Float
  1. Analisis Exploratorio de la Caracteristica Bankrupt: Aparece como una caracteristica binaria, en donde si indica cero 0, nos dice que la empresa es competente, mientras que si el valor es 1, nos indica que es probable que la empresa caiga en banca rota.
In [ ]:
datos['Bankrupt?'].plot(kind='box')      # caracteristica binaria   en la cual la categoria es puede o no caer en bancarrota 
sns.set(rc={'figure.figsize':(7,7)}) 
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Bankrupt?'].plot(kind='hist')  # corresponde a una caracteristica binaria, indica si cae o no en bancarrota
sns.set(rc={'figure.figsize':(7,7)})  # en este caso calcular varias medidas de tendencia central no genera un valor agregado
plt.show()                            # en vista que los valores o son 1 o 0, pero vamos a calcular la moda
In [ ]:
datos['Bankrupt?'].mode()   # en esta caracteristica el valor que más aparece es cero 0 aquellos que indican cero 0 indica que la empresa es competente
Out[ ]:
0    0
dtype: int64

2.Analisis Exploratorio de la Caracteristica ROA(C) before interest and depreciation before interest: El rendimiento de los activos es un índice de rentabilidad que proporciona la cantidad de ganancias que una empresa puede generar a partir de sus activos, antes de los gastos de Intereses, Depreciacion.

In [ ]:
datos['ROA(C) before interest and depreciation before interest'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)}) 
plt.show()     # se aprecia una gran cantidad de valores atípicos, los cuales serán tratados más adelante

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['ROA(C) before interest and depreciation before interest'].plot(kind='hist')    # Se aprecia un distribución concentrada al centro , con valores de la
sns.set(rc={'figure.figsize':(7,7)})                                                  # moda, media y mediana muy similar, se asemeja a una distribución normal
plt.show()
In [ ]:
datos['ROA(C) before interest and depreciation before interest'].quantile(0.25)   # primer quartil indica que un 25% de las empresas presentan un valor ROA C
                                                                                  # menor igual a 0.47
Out[ ]:
0.47652708038804703
In [ ]:
datos['ROA(C) before interest and depreciation before interest'].quantile(0.5)    # el segundo quartil indica que el 50% de las empresas presentan un valor ROA c
                                                                                  # menor o igual a 0.5027
Out[ ]:
0.502705601325988
In [ ]:
datos['ROA(C) before interest and depreciation before interest'].quantile(0.75)   # indica que el 75 % de las empresas presentan un valor menor igual a 0.535 en ROA c before interest and depreciation
Out[ ]:
0.535562813825379
In [ ]:
datos['ROA(C) before interest and depreciation before interest'].min()     # el valor más bajo en la caracteristica corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['ROA(C) before interest and depreciation before interest'].max()     # el valor más alto en la caracteristica corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['ROA(C) before interest and depreciation before interest'].mode()      # el valor que más está presenten corresponde a 0.490128
Out[ ]:
0    0.490128
dtype: float64
In [ ]:
datos['ROA(C) before interest and depreciation before interest'].median()    # la mediana nos indica que el 50% de las empresas tienen un valor menor o igual a 0.502
Out[ ]:
0.502705601325988
In [ ]:
datos['ROA(C) before interest and depreciation before interest'].mean()    # la mediana nos indica que el valor promedio de la caracteristica es de 0.50517
Out[ ]:
0.5051796332417822
In [ ]:
datos['ROA(C) before interest and depreciation before interest'].std()   # la desviación standar indica que los datos se alejan del promedio en 0.06068
Out[ ]:
0.06068563875428437

3.Analisis Exploratorio de la Caracteristica ROA(A) before interest and % after tax:

El rendimiento de los activos es un índice de rentabilidad que proporciona la cantidad de ganancias que una empresa puede generar a partir de sus activos, antes de intereses pero despues de impuestos

In [ ]:
datos['ROA(A) before interest and % after tax'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)}) 
plt.show()      # se aprecia una gran cantidad de valores atípicos, los cuales serán tratados más adelante

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['ROA(A) before interest and % after tax'].plot(kind='hist')       # se observa unadistribución con un poco hacia la izquierda , del tipo negativa
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['ROA(A) before interest and % after tax'].quantile(0.25)  # indica que el 25 % de las empresas presentan un valor menor o igual de 0.5355 
                                                                # en la caracteristica ROA(A) before interest and % after tax
Out[ ]:
0.53554295682512
In [ ]:
datos['ROA(A) before interest and % after tax'].quantile(0.5) # el segundo quantil indica que el 50 % de las empresas presentan un valor menor o igual  0.5598 en la caracteristica
Out[ ]:
0.559801569995639
In [ ]:
datos['ROA(A) before interest and % after tax'].quantile(0.75)  # el 75 % de las empresas presentan un valor menor o igual a  0.58915 en ROA(A) before interest and % after tax
Out[ ]:
0.58915721761884
In [ ]:
datos['ROA(A) before interest and % after tax'].min()   # el valor mínimo corresponde a cero 0
Out[ ]:
0.0
In [ ]:
datos['ROA(A) before interest and % after tax'].max()   # el valor más alto corresponde  a 1
Out[ ]:
1.0
In [ ]:
datos['ROA(A) before interest and % after tax'].mode()    # se observan dos modas, lo que indica que son los dos valores más repetidos en el datet para esta característica
Out[ ]:
0    0.559693
1    0.568251
dtype: float64
In [ ]:
datos['ROA(A) before interest and % after tax'].median() # el valor de la mediana nos indica que el 50% de las empresas presentan un valor menor o igual a 0.5598
                                                          # en la caracteristica  ROA(A) before interest and % after tax
Out[ ]:
0.559801569995639
In [ ]:
datos['ROA(A) before interest and % after tax'].mean()   # la media de 'ROA(A) before interest and % after tax es de 0.5586, indica que en promedoo el valor correponde a 0.5586
Out[ ]:
0.5586249158750473
In [ ]:
datos['ROA(A) before interest and % after tax'].std()   # los valores se alejan en 0.0656 del promedio
Out[ ]:
0.06562003103170724

4.Analisis Exploratorio de la Caracteristica ROA(B) before interest and depreciation after tax

El rendimiento de los activos es un índice de rentabilidad que proporciona la cantidad de ganancias que una empresa puede generar a partir de sus activos , pero en este caso antes de intereses y depreciacion pero despues de impuestos.

In [ ]:
datos['ROA(B) before interest and depreciation after tax'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)}) 
plt.show()           # se aprecia una gran cantidad de valores atípicos, los cuales serán tratados más adelante, el valor medio corresponde a 0.552

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['ROA(B) before interest and depreciation after tax'].plot(kind='hist')   # se observa una distribución con inclinación hacia la derecha, tipo negativa
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['ROA(B) before interest and depreciation after tax'].quantile(0.25)  # el primer quantil indica que el 25% de las empresas presentan un valor menor o igual a 0.5272
Out[ ]:
0.5272766208041121
In [ ]:
datos['ROA(B) before interest and depreciation after tax'].quantile(0.5)   # el quantil indica que 50% de las empresas presentan un valor menor o igual a 0.5522 en la caracteristica
Out[ ]:
0.552277959205525
In [ ]:
datos['ROA(B) before interest and depreciation after tax'].quantile(0.75) # el tercer quantil indica que el 75% de las empresas presentan un valor menor o igual a 
                                                                         #  0.5841 ROA(B) before interest and depreciation after tax
Out[ ]:
0.584105144815033
In [ ]:
datos['ROA(B) before interest and depreciation after tax'].min()   # el valor más bajo en la caracteristica corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['ROA(B) before interest and depreciation after tax'].max()   # el valor más alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['ROA(B) before interest and depreciation after tax'].mode()  # se aprecia que es una distribución multimodal presentando cuatro modas
                                                                    # siendo los valores que más se repiten los siguientes,
Out[ ]:
0    0.538787
1    0.551475
2    0.552492
3    0.558220
dtype: float64
In [ ]:
datos['ROA(B) before interest and depreciation after tax'].median() # la mediana indica que el 50% de las empresas presentan un valor menor o igual 
                                                                      # a 0.5522 en ROA B before interest and depreciation
Out[ ]:
0.552277959205525
In [ ]:
datos['ROA(B) before interest and depreciation after tax'].mean()   # el valor promedio de la caracteristica es 0.5535
Out[ ]:
0.5535887093516647
In [ ]:
datos['ROA(B) before interest and depreciation after tax'].std()   # los valores se desvian del promeido en 0.0615
Out[ ]:
0.06159480929187568

5.Analisis Exploratorio de la Caracteristica Operating Gross Margin

Representa el porcentaje de los ingresos totales que le queda a una empresa por encima de los costos directamente relacionados con la producción y la distribución.

In [ ]:
datos['Operating Gross Margin'].plot(kind='box')     # Se observan valores atípicos, el valor de la mediana alrededor de 0.60
sns.set(rc={'figure.figsize':(7,7)}) 
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Operating Gross Margin'].plot(kind='hist')     # distribución con una inclinación hacia la derecha , Negativa
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Operating Gross Margin'].quantile(0.25)   # el 25% de las empresas presentan un valor menor o igual a 0.6004 en la Operating Gross Margin
Out[ ]:
0.6004446590466854
In [ ]:
datos['Operating Gross Margin'].quantile(0.5)   # el 50% de las empresas presentan un valor menor o igual a 0.6059 en la Operating Gross Margin
Out[ ]:
0.605997492036495
In [ ]:
datos['Operating Gross Margin'].quantile(0.75)  # el 75 % de las empresas presentan un valor menor o igual a 0.613 en la operatin gross
Out[ ]:
0.613914152697502
In [ ]:
datos['Operating Gross Margin'].min()   # el valor más bajo corresponde a cero 0
Out[ ]:
0.0
In [ ]:
datos['Operating Gross Margin'].max()  # el valor más alto de la caracteristica correonde a 1
Out[ ]:
1.0
In [ ]:
datos['Operating Gross Margin'].mode()   # tiene una presencida de varias modas. Multinmodal , los valores que más se repiten 0.5989 , 0.6019 , 0.6057, 0.6064
Out[ ]:
0    0.598956
1    0.601976
2    0.605796
3    0.606495
dtype: float64
In [ ]:
datos['Operating Gross Margin'].median()   # la mediana indica que el 50%  de las empresas presentan un valor menor o igual a 0.6059 en la  operating Gross margin
Out[ ]:
0.605997492036495
In [ ]:
datos['Operating Gross Margin'].mean()  # el valor promedio de la caracteristica corresponde a 0.6079
Out[ ]:
0.6079480383703836
In [ ]:
datos['Operating Gross Margin'].std()   # los valores se alejan del promedio en 0.01693
Out[ ]:
0.016933812548221457

6.Analisis Exploratorio de la Caracteristica Realized Sales Gross Margin

El margen realizado es el margen de beneficio bruto real que obtiene al final del producto, después de exponerlo a diferentes descuentos y rebajas. El margen bruto muestra la cantidad de ganancias obtenidas antes de deducir los costos de venta, generales y administrativos (SG&A).

In [ ]:
datos['Realized Sales Gross Margin'].plot(kind='box')    # se observan valores atípicos, el valor mñinimo corresponde a cero 0, el valor de la mediana alrededor de 0.6
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Realized Sales Gross Margin'].plot(kind='hist')    # se observa la distribución negativa , valores generalmente concentrrados a partir de 0.6
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Realized Sales Gross Margin'].quantile(0.25) # indica que el 25% de las empresas presentan un valor menor o igual a 0.6004
Out[ ]:
0.6004338488591651
In [ ]:
datos['Realized Sales Gross Margin'].quantile(0.5)  # indica que el %50 de las empresas presentan un valor menor o igual a  0.6059
Out[ ]:
0.605975871661454
In [ ]:
datos['Realized Sales Gross Margin'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a 0.6138
Out[ ]:
0.6138420847806976
In [ ]:
datos['Realized Sales Gross Margin'].min()   # el valor más bajo en la caracteristica corresponde a cero 0
Out[ ]:
0.0
In [ ]:
datos['Realized Sales Gross Margin'].max()   # el valor más alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Realized Sales Gross Margin'].mode()   # distribución multimodal, los valores que más se repiten corresponden a 0.6025, 0.6047, 0.6057
Out[ ]:
0    0.602589
1    0.604715
2    0.605796
dtype: float64
In [ ]:
datos['Realized Sales Gross Margin'].median()  # el valor de la mediana indica que el 50% de las empresas presentan un valor menor o igual a 0.6079
Out[ ]:
0.605975871661454
In [ ]:
datos['Realized Sales Gross Margin'].mean()  # el promedio de la caracteristica Realized Sales Gross corresponde a 0.6079
Out[ ]:
0.6079294691769787
In [ ]:
datos['Realized Sales Gross Margin'].std()  # los valores de la caracteristica se alejan del promedio en  0.01691
Out[ ]:
0.016916070055675785

7.Analisis Exploratorio de la Caracteristica Operating Profit Rate

El margen operativo mide la cantidad de ganancias que obtiene una empresa con un dólar de ventas después de pagar los costos variables de producción, como los salarios y las materias primas, pero antes de pagar intereses o impuestos. Se calcula dividiendo los ingresos operativos de una empresa por sus ventas netas.

In [ ]:
datos['Operating Profit Rate'].plot(kind='box')    # el valor mínimo corresponde a cero 0,   se observan valores atípicos
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Operating Profit Rate'].plot(kind='hist')       # una distribución totalmente hacia la derecha
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Operating Profit Rate'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a 0.99896
Out[ ]:
0.998969203197885
In [ ]:
datos['Operating Profit Rate'].quantile(0.5)  # indica que el 50 % de las empresas presentan un valor menor o igual a  0.99902
Out[ ]:
0.9990222393745659
In [ ]:
datos['Operating Profit Rate'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.999094 en la operating Profit rate
Out[ ]:
0.999094514164357
In [ ]:
datos['Operating Profit Rate'].min()   # el valor más bajo corresponde a cero 0
Out[ ]:
0.0
In [ ]:
datos['Operating Profit Rate'].max()   # el valor más alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Operating Profit Rate'].mode()   # el valor que más se repite corresponde a 0.9989
Out[ ]:
0    0.998987
dtype: float64
In [ ]:
datos['Operating Profit Rate'].median()   # el valor de la mediana indica que el 50% de las empresas presentan un valor menor o igual a 0.9990
Out[ ]:
0.9990222393745659
In [ ]:
datos['Operating Profit Rate'].mean()  # el valor promedio de la caracteristica corresponde a 0.9987
Out[ ]:
0.9987551277900453
In [ ]:
datos['Operating Profit Rate'].std()   # los valores de la caracteristica se alejan del promedio en 0.01301
Out[ ]:
0.01301002509298413

8.Analisis Exploratorio de la Caracteristica Pre-tax net Interest Rate

La tasa de rendimiento antes de impuestos es la ganancia o pérdida de una inversión antes de que se tengan en cuenta los impuestos.

In [ ]:
datos['Pre-tax net Interest Rate'].plot(kind='box')    # el valor mínimo corresponde a cero, presencia d evalores atípicos
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Pre-tax net Interest Rate'].plot(kind='hist') 
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Pre-tax net Interest Rate'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a 0.7973
Out[ ]:
0.797385863236893
In [ ]:
datos['Pre-tax net Interest Rate'].quantile(0.5) # indica que el 50% de las empresas presentan un valor menor o igual a 0.7974
Out[ ]:
0.7974636105782309
In [ ]:
datos['Pre-tax net Interest Rate'].quantile(0.75)  # indica que el % de las empresas presentan un valor menor o igual a 0.7975
Out[ ]:
0.7975788481855891
In [ ]:
datos['Pre-tax net Interest Rate'].min()   # el valor mínimo de la caracteristica corresponde a cero
Out[ ]:
0.0
In [ ]:
datos['Pre-tax net Interest Rate'].max()  # el valor más alto que se presenta en la caracteristica corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Pre-tax net Interest Rate'].mode()  # el valor que más se repite en la Pre-tax net Interest rate corresponde a 0.797381
Out[ ]:
0    0.797381
dtype: float64
In [ ]:
datos['Pre-tax net Interest Rate'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a 0.7974
Out[ ]:
0.7974636105782309
In [ ]:
datos['Pre-tax net Interest Rate'].mean()   # el valor promedio de la caracteristica corresponde a 0.797189
Out[ ]:
0.7971897524712886
In [ ]:
datos['Pre-tax net Interest Rate'].std()   # los valores se alejan del promedio en un 0.0128
Out[ ]:
0.012868988419884656

9.Analisis Exploratorio de la Caracteristica After-tax net Interest Rate

La tasa de rendimiento real después de impuestos es el beneficio financiero real de una inversión después de contabilizar los efectos de la inflación y los impuestos.

In [ ]:
datos['After-tax net Interest Rate'].plot(kind='box')  # se visualizan valores atíicos y el valor mínimo corrresponde a cero, la mediana corresponde a 0.8
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['After-tax net Interest Rate'].plot(kind='hist')    # distribución negativa la mayoría de los datos se agrupan entre  0.8
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['After-tax net Interest Rate'].quantile(0.25)  # indica que el 25 % de las empresas presentan un valor menor o igual a  0.80931
Out[ ]:
0.809311597146491
In [ ]:
datos['After-tax net Interest Rate'].quantile(0.5)  # indica que el 50 % de las empresas presentan un valor menor o igual a  0.8093
Out[ ]:
0.809375198550956
In [ ]:
datos['After-tax net Interest Rate'].quantile(0.75)  # indica que el 75 % de las empresas presentan un valor menor o igual a  0.8094
Out[ ]:
0.8094692661348369
In [ ]:
datos['After-tax net Interest Rate'].min()  #  el valor más bajo es cero
Out[ ]:
0.0
In [ ]:
datos['After-tax net Interest Rate'].max()  # el valor más alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['After-tax net Interest Rate'].mode()  # presenta dos modas , los valores más repetidos corresponde a 0.809309 y 0.809378
Out[ ]:
0    0.809309
1    0.809378
dtype: float64
In [ ]:
datos['After-tax net Interest Rate'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a 0.8093
Out[ ]:
0.809375198550956
In [ ]:
datos['After-tax net Interest Rate'].mean()  # el promedio de la caracteristica corresponde a 0.809083
Out[ ]:
0.8090835935135369
In [ ]:
datos['After-tax net Interest Rate'].std()  # los valores de la caracteristica After-tax net intereset rate se alejan del promedio en 0.01360
Out[ ]:
0.013600653945149041

10.Analisis Exploratorio de la Caracteristica Non-industry income and expenditure/revenue

Los ingresos no operativos son la parte de los ingresos de una organización que se derivan de actividades no relacionadas con sus operaciones comerciales principales.

In [ ]:
datos['Non-industry income and expenditure/revenue'].plot(kind='box')   # el valor más bajo corresponde a cero, la mediana alrededor de  0.3 , y se observan valores atípicos
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Non-industry income and expenditure/revenue'].plot(kind='hist')    # los valores se observan muy concentrados cercanamente a 0.4
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Non-industry income and expenditure/revenue'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.3034
Out[ ]:
0.30346627659685
In [ ]:
datos['Non-industry income and expenditure/revenue'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a 0.303525
Out[ ]:
0.303525492830123
In [ ]:
datos['Non-industry income and expenditure/revenue'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a 0.303585
Out[ ]:
0.303585192461218
In [ ]:
datos['Non-industry income and expenditure/revenue'].min()  # el valor mínimo corresponde a cero
Out[ ]:
0.0
In [ ]:
datos['Non-industry income and expenditure/revenue'].max()  # el valor más alto que presenta la caracteristica es de 1
Out[ ]:
1.0
In [ ]:
datos['Non-industry income and expenditure/revenue'].mode()  # presenta varias modas, los valores que más se repiten son 0.303517, 0.303526, 0.303528
Out[ ]:
0    0.303517
1    0.303526
2    0.303528
dtype: float64
In [ ]:
datos['Non-industry income and expenditure/revenue'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.303525
Out[ ]:
0.303525492830123
In [ ]:
datos['Non-industry income and expenditure/revenue'].mean()  # el promedio de la caracteristoca corresponde a 0.3036
Out[ ]:
0.30362292364973476
In [ ]:
datos['Non-industry income and expenditure/revenue'].std()  # los valores de la caracteristica correspode a 0.01116
Out[ ]:
0.011163439838128557

11.Analisis Exploratorio de la Caracteristica Continuous interest rate (after tax)

El interés continuo ocurre cuando el interés se carga continuamente (y se agrega constantemente al capital).

In [ ]:
datos['Continuous interest rate (after tax)'].plot(kind='box')   # la mediana aparece alrededor de 0.78  ,presencia de valores atípicos
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Continuous interest rate (after tax)'].plot(kind='hist') 
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Continuous interest rate (after tax)'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a 0.78156
Out[ ]:
0.7815668165898519
In [ ]:
datos['Continuous interest rate (after tax)'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.7816
Out[ ]:
0.7816349571128741
In [ ]:
datos['Continuous interest rate (after tax)'].quantile(0.75) # indica que el 75% de las empresas presentan un valor menor o igual a 0.78173
Out[ ]:
0.7817353784192015
In [ ]:
datos['Continuous interest rate (after tax)'].min()  # el valor mínimo de la caracteristica corresponde a cero 0
Out[ ]:
0.0
In [ ]:
datos['Continuous interest rate (after tax)'].max() # el valor más alto de la caracteristica corresponde a uno
Out[ ]:
1.0
In [ ]:
datos['Continuous interest rate (after tax)'].mode()  # el valor que más se repite es 0.781683
Out[ ]:
0    0.781683
dtype: float64
In [ ]:
datos['Continuous interest rate (after tax)'].median() # la mediana indica que el 50% de las empresas presentan un valor menor o igual a 0.78163
Out[ ]:
0.7816349571128741
In [ ]:
datos['Continuous interest rate (after tax)'].mean() # el promedio de la caracteristica continuous interest corresponde a 0.78138
Out[ ]:
0.7813814325261426
In [ ]:
datos['Continuous interest rate (after tax)'].std()  # los valores se alejan del promedio en 0.0126
Out[ ]:
0.012679004028913216

12.Analisis Exploratorio de la Caracteristica Operating Expense Rate

muestra la eficiencia de la gestión de una empresa comparando el gasto operativo total (OPEX) de una empresa con las ventas netas. El índice operativo muestra qué tan eficiente es la administración de una empresa para mantener bajos los costos mientras genera ingresos o ventas.

In [ ]:
datos['Operating Expense Rate'].plot(kind='box')  # no se observan valores atípicos, el valor más bajo corresponde a cero
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Operating Expense Rate'].plot(kind='hist') 
sns.set(rc={'figure.figsize':(7,7)})                         # distribucion hacia la izquierda , del tipo positiva
plt.show()
In [ ]:
datos['Operating Expense Rate'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a 0.000156
Out[ ]:
0.000156687449242806
In [ ]:
datos['Operating Expense Rate'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a 0.000277
Out[ ]:
0.000277758858362525
In [ ]:
datos['Operating Expense Rate'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  414500000
Out[ ]:
4145000000.0
In [ ]:
datos['Operating Expense Rate'].min()    # el valor mínimo correspone a 0
Out[ ]:
0.0
In [ ]:
datos['Operating Expense Rate'].max()  # el valor maximo corresponde a 990000000
Out[ ]:
9990000000.0
In [ ]:
datos['Operating Expense Rate'].mode()   # caractersitica que presenta multimodas     1.7160,  5.53, 9.86  los valores que más se repiten
Out[ ]:
0    1.716046e-04
1    5.530000e+09
2    9.860000e+09
dtype: float64
In [ ]:
datos['Operating Expense Rate'].median()   # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.000277
Out[ ]:
0.000277758858362525
In [ ]:
datos['Operating Expense Rate'].mean()    # el valor promedio de la caracteristica correspone a 1995347312
Out[ ]:
1995347312.8027918
In [ ]:
datos['Operating Expense Rate'].std()    # la desviacion indica que los valores se alejan del promedio en 3237683890
Out[ ]:
3237683890.522487

13.Analisis Exploratorio de la Caracteristica Research and development expense rate

La Research and development expense rate mide la relación entre la capitalización de mercado de una empresa y sus gastos de investigación y desarrollo.

In [ ]:
datos['Research and development expense rate'].plot(kind='box')     # se observan valores atípicos , el valor mínimo corresponde a cero
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Research and development expense rate'].plot(kind='hist')    # distribución hacia la izquierda , del tipo positiva
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Research and development expense rate'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.000128
Out[ ]:
0.000128187953762011
In [ ]:
datos['Research and development expense rate'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a 509000000
Out[ ]:
509000000.0
In [ ]:
datos['Research and development expense rate'].quantile(0.75) # indica que el 75% de las empresas presentan un valor menor o igual a 3450000000
Out[ ]:
3450000000.0
In [ ]:
datos['Research and development expense rate'].min()  # el valor mínimo corresponde a cero
Out[ ]:
0.0
In [ ]:
datos['Research and development expense rate'].max()  # el valor máximo corresponde a 9980000000
Out[ ]:
9980000000.0
In [ ]:
datos['Research and development expense rate'].mode() # el valor que más se repite corresponde a cero 0
Out[ ]:
0    0.0
dtype: float64
In [ ]:
datos['Research and development expense rate'].median() # la medina indica que el 50% de las empresas presentan un valor menor o igual a 509000000
Out[ ]:
509000000.0
In [ ]:
datos['Research and development expense rate'].mean()  # el valor promedio de la caracteristica es de 1950427306
Out[ ]:
1950427306.056799
In [ ]:
datos['Research and development expense rate'].std()  # los valores se alejan del promedio en 2598291553
Out[ ]:
2598291553.9983416

14.Analisis Exploratorio de la Caracteristica Cash flow rate

la tasa de crecimiento a largo plazo del efectivo operativo, el dinero que realmente ingresa a las cuentas de la compañía producto de las operaciones comerciales, la IRR corresponde es una tasa de descuento que hace que el valor actual neto (VAN) de todos los flujos de efectivo sea igual a cero en un análisis de flujo de efectivo descontado.

In [ ]:
datos['Cash flow rate'].plot(kind='box')    # se observan valores atípicos , la mediana se encuentra alrededor de 0.45
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Cash flow rate'].plot(kind='hist') 
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Cash flow rate'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.46155
Out[ ]:
0.46155775311810643
In [ ]:
datos['Cash flow rate'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a 0.4650
Out[ ]:
0.465079724549793
In [ ]:
datos['Cash flow rate'].quantile(0.75) # indica que el 75% de las empresas presentan un valor menor o igual a  0.4710
Out[ ]:
0.471003917029432
In [ ]:
datos['Cash flow rate'].min()  # el valor más bajo corresponde a cero
Out[ ]:
0.0
In [ ]:
datos['Cash flow rate'].max()  # el valor más alto correpsonde a 1
Out[ ]:
1.0
In [ ]:
datos['Cash flow rate'].mode()   # tiene varias modas, los valores que más se repiten son,   0.460621, 0.460970, 0.464373
Out[ ]:
0    0.460621
1    0.460970
2    0.464373
dtype: float64
In [ ]:
datos['Cash flow rate'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.465079
Out[ ]:
0.465079724549793
In [ ]:
datos['Cash flow rate'].mean()  # el valor promeido de la caracteristica cash flow rate es de 0.467431
Out[ ]:
0.46743118577966175
In [ ]:
datos['Cash flow rate'].std()   # los valores se alejan del promedio en 0.01703
Out[ ]:
0.01703551730878539

15.Analisis Exploratorio de la Caracteristica Interest-bearing debt interest rate

Los gastos por intereses comerciales se refieren al costo de los intereses que se cargan a una empresa en función de las deudas que ha acumulado. En algunos casos, ese interés puede ser deducible de impuestos, siempre que el dinero se haya utilizado para comprar un activo relacionado específicamente con el negocio.

In [ ]:
datos['Interest-bearing debt interest rate'].plot(kind='box')    # se observa presencia de valores atípicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Interest-bearing debt interest rate'].plot(kind='hist')    # la mayoría de los valores se concentran de forma cercana a cero
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Interest-bearing debt interest rate'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.00020302
Out[ ]:
0.000203020302030203
In [ ]:
datos['Interest-bearing debt interest rate'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a 0.00032103
Out[ ]:
0.000321032103210321
In [ ]:
datos['Interest-bearing debt interest rate'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.000532
Out[ ]:
0.0005325532553255325
In [ ]:
datos['Interest-bearing debt interest rate'].min()  # el valor más bajo corresponde a cero
Out[ ]:
0.0
In [ ]:
datos['Interest-bearing debt interest rate'].max()  # el valor más alto corresponde a 990000000
Out[ ]:
990000000.0
In [ ]:
datos['Interest-bearing debt interest rate'].mode()  # el valor que más se repite corresponde a cero
Out[ ]:
0    0.0
dtype: float64
In [ ]:
datos['Interest-bearing debt interest rate'].median()  # la mediana indica que el % de las empresas presentan un valor menor o igual a   0.00032103
Out[ ]:
0.000321032103210321
In [ ]:
datos['Interest-bearing debt interest rate'].mean()  # el valor promedio de la caracteristica corresponde a 16448012
Out[ ]:
16448012.905942492
In [ ]:
datos['Interest-bearing debt interest rate'].std()   # una desviacion muy alta, lo que indica la presencia de atípicos muy notorios, 108275033 de STD
                                                    # indica que los  valores se pueden alejar del valor promedio en 108275033
Out[ ]:
108275033.5328233

16.Analisis Exploratorio de la Caracteristica Tax rate (A)

La tasa impositiva es el porcentaje al que se grava a una persona física o jurídica.

In [ ]:
datos['Tax rate (A)'].plot(kind='box')       # se muestra la presencia de valor atípicos
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Tax rate (A)'].plot(kind='hist') 
sns.set(rc={'figure.figsize':(7,7)})         #distribucion con comportamiento positiva 
plt.show()
In [ ]:
datos['Tax rate (A)'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a 0
Out[ ]:
0.0
In [ ]:
datos['Tax rate (A)'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.0734
Out[ ]:
0.0734892195566353
In [ ]:
datos['Tax rate (A)'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.20584
Out[ ]:
0.205840672132807
In [ ]:
datos['Tax rate (A)'].min()  # el valor más bajo de la caracteristica es de cero
Out[ ]:
0.0
In [ ]:
datos['Tax rate (A)'].max()  # el valor más alto de la caracteristica Tax rate (A)  corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Tax rate (A)'].mode()   # el valor qu más se repite corresponde a cero
Out[ ]:
0    0.0
dtype: float64
In [ ]:
datos['Tax rate (A)'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.073489
Out[ ]:
0.0734892195566353
In [ ]:
datos['Tax rate (A)'].mean()  # el vlaor promedio de lacaracteristica corresponde a 0.1150
Out[ ]:
0.11500074794142456
In [ ]:
datos['Tax rate (A)'].std()   # los valores se alejan del promedio en 0.13866
Out[ ]:
0.13866749672835404

17.Analisis Exploratorio de la Caracteristica Net Value Per Share (B)

El valor liquidativo por acción (NAVPS) se calcula dividiendo el valor liquidativo por el número de acciones en circulación.

In [ ]:
datos['Net Value Per Share (B)'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})                    # se observa la presencia de valores atípicos
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Net Value Per Share (B)'].plot(kind='hist')        # valores con distribuciòn del tipo positiva ,concentraciòn de los valores en cercanias a 0.2
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Net Value Per Share (B)'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.1736
Out[ ]:
0.17361257426994198
In [ ]:
datos['Net Value Per Share (B)'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.1844
Out[ ]:
0.18440015170030802
In [ ]:
datos['Net Value Per Share (B)'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.1995
Out[ ]:
0.19957018246175898
In [ ]:
datos['Net Value Per Share (B)'].min()  # el valor más bajo corresponde a cero
Out[ ]:
0.0
In [ ]:
datos['Net Value Per Share (B)'].max()  # el valor más alto de la caracteristica es de 1
Out[ ]:
1.0
In [ ]:
datos['Net Value Per Share (B)'].mode()   # el valor que más se repite corrsponde a 0.176984
Out[ ]:
0    0.176984
dtype: float64
In [ ]:
datos['Net Value Per Share (B)'].median()   # la mediana nos indica que el 50% de las empresas presentan un valor menor o igual a  0.1844
Out[ ]:
0.18440015170030802
In [ ]:
datos['Net Value Per Share (B)'].mean()   # el valor promedio de la caracteristica es de 0.19066
Out[ ]:
0.19066057949747367
In [ ]:
datos['Net Value Per Share (B)'].std()  # los valores se desvian del promedio en 0.033389
Out[ ]:
0.033389768351330965

18.Analisis Exploratorio de la Caracteristica Net Value Per Share (A)

El valor liquidativo por acción (NAVPS) se calcula dividiendo el valor liquidativo por el número de acciones en circulación.

In [ ]:
datos['Net Value Per Share (A)'].plot(kind='box')     # se observan valores atípicos,  la mediana  se observa cercana a 0.2   
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Net Value Per Share (A)'].plot(kind='hist')   #  se observan los valores concentrados en la cercanía a 0.2   , distribución del tipo positiva (izquierda)
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Net Value Per Share (A)'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a 0.173
Out[ ]:
0.17361257426994198
In [ ]:
datos['Net Value Per Share (A)'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.1844
Out[ ]:
0.18440015170030802
In [ ]:
datos['Net Value Per Share (A)'].quantile(0.75)  # indica que el 75 % de las empresas presentan un valor menor o igual a 0.1995
Out[ ]:
0.19957018246175898
In [ ]:
datos['Net Value Per Share (A)'].min()  # el valor más bajo es de cero
Out[ ]:
0.0
In [ ]:
datos['Net Value Per Share (A)'].max()  # el valor más alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Net Value Per Share (A)'].mode()  # el valor que más se repite es 0.176984
Out[ ]:
0    0.176984
dtype: float64
In [ ]:
datos['Net Value Per Share (A)'].median() # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.1844
Out[ ]:
0.18440015170030802
In [ ]:
datos['Net Value Per Share (A)'].mean()   # el valor promedio de la caracteristica 0.1906
Out[ ]:
0.1906331789677462
In [ ]:
datos['Net Value Per Share (A)'].std()   # los valores se desvian del promedio en 0.0334
Out[ ]:
0.03347351417242891

19.Analisis Exploratorio de la Caracteristica Net Value Per Share (C)

El valor liquidativo por acción (NAVPS) se calcula dividiendo el valor liquidativo por el número de acciones en circulación.

In [ ]:
datos['Net Value Per Share (C)'].plot(kind='box')   # se observan valores atípicos , la mediana cercana a 0.2 , el valor mínimo cero
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Net Value Per Share (C)'].plot(kind='hist') 
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Net Value Per Share (C)'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.17367
Out[ ]:
0.1736757827314485
In [ ]:
datos['Net Value Per Share (C)'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.1844
Out[ ]:
0.18440015170030802
In [ ]:
datos['Net Value Per Share (C)'].quantile(0.75)  # indica que el 75 % de las empresas presentan un valor menor o igual a  0.1996
Out[ ]:
0.19961232143609603
In [ ]:
datos['Net Value Per Share (C)'].min()  # el valor más bajo es de 0
Out[ ]:
0.0
In [ ]:
datos['Net Value Per Share (C)'].max()  # el valor más alto es de 1
Out[ ]:
1.0
In [ ]:
datos['Net Value Per Share (C)'].mode()  # el valor que más se repite en 0.17684
Out[ ]:
0    0.176984
dtype: float64
In [ ]:
datos['Net Value Per Share (C)'].median()  # la mediana  indica que el 50% de las empresas presentan un valor menor o igual a  0.184
Out[ ]:
0.18440015170030802
In [ ]:
datos['Net Value Per Share (C)'].mean()  # el valor promedio es de 0.19067
Out[ ]:
0.19067237025316164
In [ ]:
datos['Net Value Per Share (C)'].std()  # los valores de la caracteristica se desvian o se alejan del promedio en  0.0334
Out[ ]:
0.033480137670409124

20.Analisis Exploratorio de la Caracteristica Persistent EPS in the Last Four Seasons

Brinda la información del valor de las acciones en los ultimas 4 temporadas. Las ganancias por acción (EPS) se calculan como las ganancias de una empresa divididas por las acciones en circulación de sus acciones ordinarias.

In [ ]:
datos['Persistent EPS in the Last Four Seasons'].plot(kind='box')    # se bservan valores atípicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Persistent EPS in the Last Four Seasons'].plot(kind='hist')     # concentración de los valores en cercanía  0.25
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Persistent EPS in the Last Four Seasons'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.2147
Out[ ]:
0.21471116573697602
In [ ]:
datos['Persistent EPS in the Last Four Seasons'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.2245
Out[ ]:
0.22454382149948002
In [ ]:
datos['Persistent EPS in the Last Four Seasons'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.23882
Out[ ]:
0.2388200813085
In [ ]:
datos['Persistent EPS in the Last Four Seasons'].min()  # el valor más bajo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Persistent EPS in the Last Four Seasons'].max()   # el valor más alto es de 1
Out[ ]:
1.0
In [ ]:
datos['Persistent EPS in the Last Four Seasons'].mode()  # el valor que más se repite es 0.2149
Out[ ]:
0    0.2149
dtype: float64
In [ ]:
datos['Persistent EPS in the Last Four Seasons'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.2245
Out[ ]:
0.22454382149948002
In [ ]:
datos['Persistent EPS in the Last Four Seasons'].mean()  # valor promedio de lacaracteristica corresponde a 0.22881
Out[ ]:
0.22881285256452713
In [ ]:
datos['Persistent EPS in the Last Four Seasons'].std()   # desviacion indica que los valores se alejan del promedio en 0.0332626
Out[ ]:
0.03326261307597681

21.Analisis Exploratorio de la Caracteristica Cash Flow Per Share

El flujo de efectivo por acción son las ganancias después de impuestos más la depreciación por acción que funciona como una medida de la solidez financiera de una empresa. Muchos analistas financieros ponen más énfasis en el flujo de caja por acción que en las ganancias por acción (EPS).

In [ ]:
datos['Cash Flow Per Share'].plot(kind='box')    # se observan calores atípicos
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Cash Flow Per Share'].plot(kind='hist') 
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Cash Flow Per Share'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.31774
Out[ ]:
0.317747754120393
In [ ]:
datos['Cash Flow Per Share'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.3224
Out[ ]:
0.322487090613284
In [ ]:
datos['Cash Flow Per Share'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.328623
Out[ ]:
0.3286234703260945
In [ ]:
datos['Cash Flow Per Share'].min()   # el valor maa bajo es de cero
Out[ ]:
0.0
In [ ]:
datos['Cash Flow Per Share'].max()   # el valor más alto es de 1
Out[ ]:
1.0
In [ ]:
datos['Cash Flow Per Share'].mode()   # presenta varias modas , los valores que más que se repiten
Out[ ]:
0    0.319198
1    0.320506
2    0.322558
dtype: float64
In [ ]:
datos['Cash Flow Per Share'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.32248
Out[ ]:
0.322487090613284
In [ ]:
datos['Cash Flow Per Share'].mean()  # el promedio de la caracteristica de cash flow per share corresponde a 0.3234
Out[ ]:
0.32348191216983185
In [ ]:
datos['Cash Flow Per Share'].std()   # la desviacion indica que los valores se alejan del promedio en 0.0176
Out[ ]:
0.017610912958343786

22.Analisis Exploratorio de la Caracteristica Revenue Per Share (Yuan ¥)

Es un índice financiero, que divide las ganancias netas disponibles para los accionistas comunes por el promedio de acciones en circulación durante un cierto período de tiempo.

In [ ]:
datos['Revenue Per Share (Yuan ¥)'].plot(kind='box')      # valores con atípicos
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Revenue Per Share (Yuan ¥)'].plot(kind='hist') 
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Revenue Per Share (Yuan ¥)'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.0156
Out[ ]:
0.01563138073415305
In [ ]:
datos['Revenue Per Share (Yuan ¥)'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.0273
Out[ ]:
0.0273757127516373
In [ ]:
datos['Revenue Per Share (Yuan ¥)'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.046
Out[ ]:
0.0463572152396509
In [ ]:
datos['Revenue Per Share (Yuan ¥)'].min()  # el valor más bajo es cero
Out[ ]:
0.0
In [ ]:
datos['Revenue Per Share (Yuan ¥)'].max()  # el valormás alto de 302000000
Out[ ]:
3020000000.0
In [ ]:
datos['Revenue Per Share (Yuan ¥)'].mode()  # el valor que más se repite corresponde a  0.01776
Out[ ]:
0    0.017756
dtype: float64
In [ ]:
datos['Revenue Per Share (Yuan ¥)'].median()  # lamediana indica que el % de las empresas presentan un valor menor o igual a  0.027375
Out[ ]:
0.0273757127516373
In [ ]:
datos['Revenue Per Share (Yuan ¥)'].mean()     # el valor promedio de 1328640
Out[ ]:
1328640.6020960642
In [ ]:
datos['Revenue Per Share (Yuan ¥)'].std()   # los valores pueden alejarse del promedio en 51707089
Out[ ]:
51707089.76790668

23.Analisis Exploratorio de la Caracteristica Operating Profit Per Share (Yuan ¥)

Ganancias por acción ordinarias significa las ganancias netas consolidadas divididas por el número de acciones ordinarias en circulación al final del período de rendimiento.

In [ ]:
datos['Operating Profit Per Share (Yuan ¥)'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Operating Profit Per Share (Yuan ¥)'].plot(kind='hist') 
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Operating Profit Per Share (Yuan ¥)'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a   0.096
Out[ ]:
0.0960833808321798
In [ ]:
datos['Operating Profit Per Share (Yuan ¥)'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a  0.1042
Out[ ]:
0.104226040224737
In [ ]:
datos['Operating Profit Per Share (Yuan ¥)'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a 0.11615
Out[ ]:
0.1161550362348345
In [ ]:
datos['Operating Profit Per Share (Yuan ¥)'].min()  # el valormás bajo es cero
Out[ ]:
0.0
In [ ]:
datos['Operating Profit Per Share (Yuan ¥)'].max()  # el valormás alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Operating Profit Per Share (Yuan ¥)'].mode()   # el valor que más se repite es 0.097
Out[ ]:
0    0.097142
dtype: float64
In [ ]:
datos['Operating Profit Per Share (Yuan ¥)'].median()
Out[ ]:
0.104226040224737
In [ ]:
datos['Operating Profit Per Share (Yuan ¥)'].mean()   # el promedio es de 0.1090
Out[ ]:
0.10909073887546941
In [ ]:
datos['Operating Profit Per Share (Yuan ¥)'].std()
Out[ ]:
0.027942244774416092

24.Analisis Exploratorio de la Caracteristica Per Share Net profit before tax (Yuan ¥)

las ganancias netas consolidadas divididas por el número de acciones ordinarias en circulación al final del período de rendimiento, antes de impuestos

In [ ]:
datos['Per Share Net profit before tax (Yuan ¥)'].plot(kind='box')    # se observan valores atipicos, medina cercana a 0.2
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Per Share Net profit before tax (Yuan ¥)'].plot(kind='hist')       # valores concetrados cercanamente a 1.8 y 0.2
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Per Share Net profit before tax (Yuan ¥)'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a   0.17036
Out[ ]:
0.170369812457634
In [ ]:
datos['Per Share Net profit before tax (Yuan ¥)'].quantile(0.5)    # indica que el 50% de las empresas presentan un valor menor o igual a  0.179
Out[ ]:
0.179709271672818
In [ ]:
datos['Per Share Net profit before tax (Yuan ¥)'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.193
Out[ ]:
0.193492505837162
In [ ]:
datos['Per Share Net profit before tax (Yuan ¥)'].min()   # el valor mínimo es de cero
Out[ ]:
0.0
In [ ]:
datos['Per Share Net profit before tax (Yuan ¥)'].max()  # el valor más alto es de 1
Out[ ]:
1.0
In [ ]:
datos['Per Share Net profit before tax (Yuan ¥)'].mode()  # el valor que más se repite es de 0.170144
Out[ ]:
0    0.170144
dtype: float64
In [ ]:
datos['Per Share Net profit before tax (Yuan ¥)'].median()
Out[ ]:
0.179709271672818
In [ ]:
datos['Per Share Net profit before tax (Yuan ¥)'].mean()  # el promedio es d e0.1843
Out[ ]:
0.1843605776420337
In [ ]:
datos['Per Share Net profit before tax (Yuan ¥)'].std()
Out[ ]:
0.03318020898090537

25.Analisis Exploratorio de la Caracteristica Realized Sales Gross Profit Growth Rate

El margen bruto representa la cantidad de ingresos por ventas totales que la empresa retiene después de incurrir en los costos directos (COGS). Este indicador nos dirá el % de crecimiento del Sales Gross Profit

In [ ]:
datos['Realized Sales Gross Profit Growth Rate'].plot(kind='box')       # se observan valores atípicos
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Realized Sales Gross Profit Growth Rate'].plot(kind='hist') 
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Realized Sales Gross Profit Growth Rate'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.022064
Out[ ]:
0.022064532735505453
In [ ]:
datos['Realized Sales Gross Profit Growth Rate'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.022102
Out[ ]:
0.0221023731764072
In [ ]:
datos['Realized Sales Gross Profit Growth Rate'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.022153
Out[ ]:
0.022153148426612798
In [ ]:
datos['Realized Sales Gross Profit Growth Rate'].min()  # el valor más bajo corresponde a cero
Out[ ]:
0.0
In [ ]:
datos['Realized Sales Gross Profit Growth Rate'].max()  # el valor mas alto es de 1
Out[ ]:
1.0
In [ ]:
datos['Realized Sales Gross Profit Growth Rate'].mode()   # el valor más se repit corresponde a 0.022092
Out[ ]:
0    0.022092
dtype: float64
In [ ]:
datos['Realized Sales Gross Profit Growth Rate'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.0221
Out[ ]:
0.0221023731764072
In [ ]:
datos['Realized Sales Gross Profit Growth Rate'].mean()
Out[ ]:
0.02240785447416586
In [ ]:
datos['Realized Sales Gross Profit Growth Rate'].std()
Out[ ]:
0.012079270152911575

26.Analisis Exploratorio de la Caracteristica Operating Profit Growth Rate

El crecimiento de la utilidad operativa muestra el aumento porcentual de la utilidad operativa durante el último año

Las tasas de crecimiento se refieren al cambio porcentual de una variable específica dentro de un período de tiempo específico.

In [ ]:
datos['Operating Profit Growth Rate'].plot(kind='box')      # se observan valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Operating Profit Growth Rate'].plot(kind='hist')        # se observa concetración de valores cercanamente a 0.83 y 0.9
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Operating Profit Growth Rate'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.8479
Out[ ]:
0.8479841081819834
In [ ]:
datos['Operating Profit Growth Rate'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a  0.84804
Out[ ]:
0.8480435337457679
In [ ]:
datos['Operating Profit Growth Rate'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a  0.8481
Out[ ]:
0.8481225403945605
In [ ]:
datos['Operating Profit Growth Rate'].min()     # el valormínimo es de cero
Out[ ]:
0.0
In [ ]:
datos['Operating Profit Growth Rate'].max()   # el valor más alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Operating Profit Growth Rate'].mode()   # presenta varios valores que son los que más se repiten
Out[ ]:
0    0.847957
1    0.847982
2    0.848005
3    0.848022
4    0.848045
5    0.848057
dtype: float64
In [ ]:
datos['Operating Profit Growth Rate'].median()
Out[ ]:
0.8480435337457679
In [ ]:
datos['Operating Profit Growth Rate'].mean()  # el promedio de los valores de la caracteristica es de 0.847979
Out[ ]:
0.8479799951688058
In [ ]:
datos['Operating Profit Growth Rate'].std()
Out[ ]:
0.01075247740540135

27.Analisis Exploratorio de la Caracteristica After-tax Net Profit Growth Rate

El crecimiento de la utilidad muestra el aumento porcentual de la utililidad durante el último año, despues de impuesto. Las tasas de crecimiento se refieren al cambio porcentual de una variable específica dentro de un período de tiempo específico.

In [ ]:
datos['After-tax Net Profit Growth Rate'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})                                              # se observan valors atipicos, median cercana a  0.68
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['After-tax Net Profit Growth Rate'].plot(kind='hist') 
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['After-tax Net Profit Growth Rate'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.6892
Out[ ]:
0.6892699337448114
In [ ]:
datos['After-tax Net Profit Growth Rate'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.6896
Out[ ]:
0.689438526343149
In [ ]:
datos['After-tax Net Profit Growth Rate'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.6896
Out[ ]:
0.6896471679790515
In [ ]:
datos['After-tax Net Profit Growth Rate'].min()   # el valor mínimo corresponde  a cero 0
Out[ ]:
0.0
In [ ]:
datos['After-tax Net Profit Growth Rate'].max()  # el valor más alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['After-tax Net Profit Growth Rate'].mode()   # multimodal, presenta varios números que son los que más se repiten
Out[ ]:
0    0.689449
1    0.689525
2    0.689702
dtype: float64
In [ ]:
datos['After-tax Net Profit Growth Rate'].median()   # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.68943
Out[ ]:
0.689438526343149
In [ ]:
datos['After-tax Net Profit Growth Rate'].mean()   # el valor promedio de la caracteristica es de 0.6891
Out[ ]:
0.689146118568132
In [ ]:
datos['After-tax Net Profit Growth Rate'].std()
Out[ ]:
0.013853022260934765

28.Analisis Exploratorio de la Caracteristica Regular Net Profit Growth Rate

Expresado como porcentaje, el margen de beneficio neto muestra cuánto de cada dólar recaudado por una empresa como ingresos se traduce en beneficios, por lo tanto el Net Profit Growth Rate, indica el porcentaje de crecimiento de este indicar de un periodo a otro.

In [ ]:
datos['Regular Net Profit Growth Rate'].plot(kind='box')   # presencia de valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Regular Net Profit Growth Rate'].plot(kind='hist') 
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Regular Net Profit Growth Rate'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.6892
Out[ ]:
0.6892702655632059
In [ ]:
datos['Regular Net Profit Growth Rate'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a  0.6894
Out[ ]:
0.689438555196922
In [ ]:
datos['Regular Net Profit Growth Rate'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a 0.68964
Out[ ]:
0.6896470092832976
In [ ]:
datos['Regular Net Profit Growth Rate'].min()   # el valor mìnimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Regular Net Profit Growth Rate'].max()  # el valor más alto es de 1
Out[ ]:
1.0
In [ ]:
datos['Regular Net Profit Growth Rate'].mode()   # el valor mas se repite corresponde a 0.689
Out[ ]:
0    0.689449
dtype: float64
In [ ]:
datos['Regular Net Profit Growth Rate'].median()
Out[ ]:
0.689438555196922
In [ ]:
datos['Regular Net Profit Growth Rate'].mean()
Out[ ]:
0.6891500117795625
In [ ]:
datos['Regular Net Profit Growth Rate'].std()
Out[ ]:
0.013910283414010596

29.Analisis Exploratorio de la Caracteristica Continuous Net Profit Growth Rate

Las tasas de crecimiento se refieren al cambio porcentual de una variable específica dentro de un período de tiempo específico, en este caso mide el mide el crecimiento de un periodo a otro de la utilidad neta.

In [ ]:
datos['Continuous Net Profit Growth Rate'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Continuous Net Profit Growth Rate'].plot(kind='hist') 
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Continuous Net Profit Growth Rate'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.2175
Out[ ]:
0.2175795122117655
In [ ]:
datos['Continuous Net Profit Growth Rate'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a   0.217598
Out[ ]:
0.21759804696196303
In [ ]:
datos['Continuous Net Profit Growth Rate'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.217621
Out[ ]:
0.217621501194243
In [ ]:
datos['Continuous Net Profit Growth Rate'].min()   # el valor más bajo es cero
Out[ ]:
0.0
In [ ]:
datos['Continuous Net Profit Growth Rate'].max()   # el valor más alto es de 1
Out[ ]:
1.0
In [ ]:
datos['Continuous Net Profit Growth Rate'].mode()   # el valor que más se repite es 0.2175
Out[ ]:
0    0.21758
dtype: float64
In [ ]:
datos['Continuous Net Profit Growth Rate'].median()   # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.21759
Out[ ]:
0.21759804696196303
In [ ]:
datos['Continuous Net Profit Growth Rate'].mean()
Out[ ]:
0.2176390129969667
In [ ]:
datos['Continuous Net Profit Growth Rate'].std()
Out[ ]:
0.010062963146116098

30.Analisis Exploratorio de la Caracteristica Total Asset Growth Rate

La tasa de crecimiento de activos muestra la rapidez con la que una empresa ha aumentado sus activos. Se calcula como un cambio porcentual en los activos durante un período determinado.

In [ ]:
datos['Total Asset Growth Rate'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})                               # se observan valores atipicos , la mediana se observa cercana a 640000000
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Total Asset Growth Rate'].plot(kind='hist')   # se observa una distribución multimodal
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Total Asset Growth Rate'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  4860000000
Out[ ]:
4860000000.0
In [ ]:
datos['Total Asset Growth Rate'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  6400000000
Out[ ]:
6400000000.0
In [ ]:
datos['Total Asset Growth Rate'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  7390000000
Out[ ]:
7390000000.0
In [ ]:
datos['Total Asset Growth Rate'].min()   # el valor más bajo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Total Asset Growth Rate'].max()  # el valor más alto corresponde a 9990000000
Out[ ]:
9990000000.0
In [ ]:
datos['Total Asset Growth Rate'].mode()    # presenta dos modas
Out[ ]:
0    6.370000e+09
1    6.400000e+09
dtype: float64
In [ ]:
datos['Total Asset Growth Rate'].median()   # la mediana  indica que el 50 % de las empresas presentan un valor menor o igual a  6400000000
Out[ ]:
6400000000.0
In [ ]:
datos['Total Asset Growth Rate'].mean()   # el promedio de la caracteristica corresponde a 550809659
Out[ ]:
5508096595.248731
In [ ]:
datos['Total Asset Growth Rate'].std()   # se desvia del promedio en   2897717771
Out[ ]:
2897717771.1697345

31.Analisis Exploratorio de la Caracteristica Net Value Growth Rate

La tasa de crecimiento del Valor Neto. el NET VALUE se puede interpretar como, El valor actual neto (VAN) es la diferencia entre el valor presente de las entradas de efectivo y el valor presente de las salidas de efectivo durante un período de tiempo. El VPN se utiliza en el presupuesto de capital y la planificación de inversiones para analizar la rentabilidad de una inversión o proyecto proyectado.

In [ ]:
datos['Net Value Growth Rate'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Net Value Growth Rate'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Net Value Growth Rate'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.0004409
Out[ ]:
0.000440968886826437
In [ ]:
datos['Net Value Growth Rate'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a  0.000461
Out[ ]:
0.000461955522207628
In [ ]:
datos['Net Value Growth Rate'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.0004993
Out[ ]:
0.000499362141038075
In [ ]:
datos['Net Value Growth Rate'].min()   # el valorminimo corresponde a cero
Out[ ]:
0.0
In [ ]:
datos['Net Value Growth Rate'].max()   # el valor maximo o mas alto coresponde a 9330000000
Out[ ]:
9330000000.0
In [ ]:
datos['Net Value Growth Rate'].mode()  # presenta dos valores como la moda , que se repiden varias veces ,
Out[ ]:
0    0.000445
1    0.000449
dtype: float64
In [ ]:
datos['Net Value Growth Rate'].median()   # la mediana indica que el  50 % de las empresas presentan un valor menor o igual a  0.0004619
Out[ ]:
0.000461955522207628
In [ ]:
datos['Net Value Growth Rate'].mean()
Out[ ]:
1566212.0552410616
In [ ]:
datos['Net Value Growth Rate'].std()
Out[ ]:
114159389.51833564

32.Analisis Exploratorio de la Caracteristica Total Asset Return Growth Rate Ratio

Presenta la tasa de crecimiento del Asset Return, y el Asset Return es una relación que mide las ganancias de una empresa antes de intereses e impuestos (EBIT) en relación con sus activos netos totales. Se define como la relación entre el ingreso neto y los activos promedio totales.

In [ ]:
datos['Total Asset Return Growth Rate Ratio'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Total Asset Return Growth Rate Ratio'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Total Asset Return Growth Rate Ratio'].quantile(0.25)    # indica que el 25 % de las empresas presentan un valor menor o igual a  0.26375
Out[ ]:
0.263758926420651
In [ ]:
datos['Total Asset Return Growth Rate Ratio'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a  0.26404
Out[ ]:
0.26404954503422895
In [ ]:
datos['Total Asset Return Growth Rate Ratio'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.2643
Out[ ]:
0.264388341065032
In [ ]:
datos['Total Asset Return Growth Rate Ratio'].min()   # el valor minimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Total Asset Return Growth Rate Ratio'].max()   # el valormás alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Total Asset Return Growth Rate Ratio'].mode()  # presenta varias modas, multimodal
Out[ ]:
0    0.263910
1    0.263975
2    0.263994
3    0.264057
4    0.264097
5    0.264140
dtype: float64
In [ ]:
datos['Total Asset Return Growth Rate Ratio'].median()   # la medana indica que el 50% de las empresas presentan un valor menor o igual a  0.2640
Out[ ]:
0.26404954503422895
In [ ]:
datos['Total Asset Return Growth Rate Ratio'].mean()   # el valor promedio corresponde a 0.264247
Out[ ]:
0.2642475118758422
In [ ]:
datos['Total Asset Return Growth Rate Ratio'].std()   # los valores se alejan del promedio en 0.009634
Out[ ]:
0.009634208862611621

33.Analisis Exploratorio de la Caracteristica Cash Reinvestment %

Es un índice de valoración que se utiliza para medir el porcentaje del flujo de efectivo anual que la empresa invierte en el negocio como una nueva inversión. Esta relación permite a los analistas comprender el grado en que los ingresos netos se reinvierten en el negocio.

In [ ]:
datos['Cash Reinvestment %'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Cash Reinvestment %'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Cash Reinvestment %'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.3747
Out[ ]:
0.37474851905666695
In [ ]:
datos['Cash Reinvestment %'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a  0.38042
Out[ ]:
0.380425468499683
In [ ]:
datos['Cash Reinvestment %'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.386
Out[ ]:
0.386731120301032
In [ ]:
datos['Cash Reinvestment %'].min()   # el valor minimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Cash Reinvestment %'].max()   # el valor maximo es de 1
Out[ ]:
1.0
In [ ]:
datos['Cash Reinvestment %'].mode()
Out[ ]:
0    0.375387
1    0.375889
dtype: float64
In [ ]:
datos['Cash Reinvestment %'].median()
Out[ ]:
0.380425468499683
In [ ]:
datos['Cash Reinvestment %'].mean()   # el valor promedio de 0.37967
Out[ ]:
0.37967667232266256
In [ ]:
datos['Cash Reinvestment %'].std()   # los valores se alejan del promedio en 0.0207
Out[ ]:
0.020736565809616768

34.Analisis Exploratorio de la Caracteristica Current Ratio

Es un índice de liquidez que mide la capacidad de una empresa para pagar obligaciones a corto plazo o las que vencen dentro de un año. Les dice a los inversores y analistas cómo una empresa puede maximizar los activos corrientes en su balance para satisfacer su deuda actual y otras cuentas por pagar.

In [ ]:
datos['Current Ratio'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Current Ratio'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Current Ratio'].quantile(0.25)   # indica que el25 % de las empresas presentan un valor menor o igual a  0.0075
Out[ ]:
0.00755504663011972
In [ ]:
datos['Current Ratio'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.010
Out[ ]:
0.0105871744549939
In [ ]:
datos['Current Ratio'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.0162
Out[ ]:
0.0162695280201934
In [ ]:
datos['Current Ratio'].min()    # el valor minimo corresponde a cero
Out[ ]:
0.0
In [ ]:
datos['Current Ratio'].max()    # el valor maximo corresponde a 2750000000
Out[ ]:
2750000000.0
In [ ]:
datos['Current Ratio'].mode()   # multimodal, presenta muchas moas , valores que mas se repiten
Out[ ]:
0    0.005888
1    0.006145
2    0.006916
3    0.007071
4    0.007139
5    0.009163
6    0.012144
7    0.013174
dtype: float64
In [ ]:
datos['Current Ratio'].median()
Out[ ]:
0.0105871744549939
In [ ]:
datos['Current Ratio'].mean()
Out[ ]:
403284.9542449723
In [ ]:
datos['Current Ratio'].std()
Out[ ]:
33302155.82548018

35.Analisis Exploratorio de la Caracteristica Quick Ratio

Mide la capacidad de una empresa para pagar sus pasivos corrientes sin necesidad de vender su inventario u obtener financiación adicional.

In [ ]:
datos['Quick Ratio'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Quick Ratio'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Quick Ratio'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.00472
Out[ ]:
0.004725903227376115
In [ ]:
datos['Quick Ratio'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.0074
Out[ ]:
0.00741247206754445
In [ ]:
datos['Quick Ratio'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.01224
Out[ ]:
0.01224910697241505
In [ ]:
datos['Quick Ratio'].min()   # el valor minimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Quick Ratio'].max()   # el valor mas alto corresponde a 9230000000
Out[ ]:
9230000000.0
In [ ]:
datos['Quick Ratio'].mode()   # el valor que mas se repite corresponde a 0.005432
Out[ ]:
0    0.005432
dtype: float64
In [ ]:
datos['Quick Ratio'].median()
Out[ ]:
0.00741247206754445
In [ ]:
datos['Quick Ratio'].mean()   # el valor promedio de la caracteristica es de 8376594
Out[ ]:
8376594.819684908
In [ ]:
datos['Quick Ratio'].std()
Out[ ]:
244684748.4468722

36.Analisis Exploratorio de la Caracteristica Interest Expense Ratio

Se calcula dividiendo el gasto total por intereses de su negocio en todos los préstamos para un año fiscal o calendario por las ganancias antes de intereses, impuestos sobre la renta, depreciación o amortización (comúnmente conocido como EBITDA)

In [ ]:
datos['Interest Expense Ratio'].plot(kind='box')     # se observa la presencia de valores atipicos
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Interest Expense Ratio'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Interest Expense Ratio'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.6306
Out[ ]:
0.63061225188696
In [ ]:
datos['Interest Expense Ratio'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.63069
Out[ ]:
0.630698209613567
In [ ]:
datos['Interest Expense Ratio'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.6311
Out[ ]:
0.631125258558102
In [ ]:
datos['Interest Expense Ratio'].min()   # el valor mas bajo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Interest Expense Ratio'].max()   # el valor mas alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Interest Expense Ratio'].mode()   # el valor que mas se repite corresponde a 0.6306
Out[ ]:
0    0.630612
dtype: float64
In [ ]:
datos['Interest Expense Ratio'].median()
Out[ ]:
0.630698209613567
In [ ]:
datos['Interest Expense Ratio'].mean()
Out[ ]:
0.6309910117124122
In [ ]:
datos['Interest Expense Ratio'].std()
Out[ ]:
0.01123846150405023

37.Analisis Exploratorio de la Caracteristica Total debt/Total net worth

Es una relación de apalancamiento que define la cantidad total de deuda en relación con los activos que posee una empresa. Con esta métrica, los analistas pueden comparar el apalancamiento de una empresa con el de otras empresas de la misma industria.

In [ ]:
datos['Total debt/Total net worth'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Total debt/Total net worth'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Total debt/Total net worth'].quantile(0.25)    # indica que el 25% de las empresas presentan un valor menor o igual a  0.003007
Out[ ]:
0.00300704912501482
In [ ]:
datos['Total debt/Total net worth'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.00554
Out[ ]:
0.00554628439070209
In [ ]:
datos['Total debt/Total net worth'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.0092
Out[ ]:
0.009273292661797026
In [ ]:
datos['Total debt/Total net worth'].min()   # el valor minimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Total debt/Total net worth'].max()   # el valor mas alto es de 9940000000
Out[ ]:
9940000000.0
In [ ]:
datos['Total debt/Total net worth'].mode()   # multimodal, variosnumeros que se repiten varias veces
Out[ ]:
0    0.001517
1    0.003187
2    0.003414
dtype: float64
In [ ]:
datos['Total debt/Total net worth'].median()   # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.005546
Out[ ]:
0.00554628439070209
In [ ]:
datos['Total debt/Total net worth'].mean()   # promedio es de 4416336
Out[ ]:
4416336.714259364
In [ ]:
datos['Total debt/Total net worth'].std()   # se aleja del promedio en 168406905
Out[ ]:
168406905.28151137

38.Analisis Exploratorio de la Caracteristica Debt ratio %

Es un índice financiero que mide el grado de apalancamiento de una empresa. La razón de la deuda se define como la razón entre la deuda total y los activos totales.

In [ ]:
datos['Debt ratio %'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Debt ratio %'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Debt ratio %'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a   0.07289
Out[ ]:
0.0728905281615624
In [ ]:
datos['Debt ratio %'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.1114
Out[ ]:
0.11140671765879599
In [ ]:
datos['Debt ratio %'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a  0.1488
Out[ ]:
0.14880430510626702
In [ ]:
datos['Debt ratio %'].min()   # el valor minimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Debt ratio %'].max()  # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['Debt ratio %'].mode()   # una distribución multimodal según este resultado tenemos 8 distintos valores que son los que más se repiten
Out[ ]:
0    0.089156
1    0.106721
2    0.112918
3    0.115458
4    0.119479
5    0.123409
6    0.128216
7    0.140158
dtype: float64
In [ ]:
datos['Debt ratio %'].median()
Out[ ]:
0.11140671765879599
In [ ]:
datos['Debt ratio %'].mean()
Out[ ]:
0.11317708497306005
In [ ]:
datos['Debt ratio %'].std()
Out[ ]:
0.053920306063082755

39.Analisis Exploratorio de la Caracteristica Net worth/Assets

El patrimonio neto es el valor de todos los activos, menos el total de todos los pasivos. Dicho de otra manera, el valor neto es lo que se posee menos lo que se debe

In [ ]:
datos['Net worth/Assets'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Net worth/Assets'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Net worth/Assets'].quantile(0.25)  # indica que el 25 % de las empresas presentan un valor menor o igual a  0.85119
Out[ ]:
0.8511956948937329
In [ ]:
datos['Net worth/Assets'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.8885
Out[ ]:
0.8885932823412042
In [ ]:
datos['Net worth/Assets'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a  0.9271
Out[ ]:
0.927109471838438
In [ ]:
datos['Net worth/Assets'].min()   # el valor minimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Net worth/Assets'].max()   # el valormas alto es de 1
Out[ ]:
1.0
In [ ]:
datos['Net worth/Assets'].mode()   # mulimodal
Out[ ]:
0    0.859842
1    0.871784
2    0.876591
3    0.880521
4    0.884542
5    0.887082
6    0.893279
7    0.910844
dtype: float64
In [ ]:
datos['Net worth/Assets'].median()
Out[ ]:
0.8885932823412042
In [ ]:
datos['Net worth/Assets'].mean()
Out[ ]:
0.8868229150269425
In [ ]:
datos['Net worth/Assets'].std()
Out[ ]:
0.05392030606308272

40.Analisis Exploratorio de la Caracteristica Long-term fund suitability ratio (A) Índice que indica el nivel optimo o idoneo de endeudamiento a largo plazo

In [ ]:
datos['Long-term fund suitability ratio (A)'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Long-term fund suitability ratio (A)'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Long-term fund suitability ratio (A)'].quantile(0.25)   # indica que el 25 % de las empresas presentan un valor menor o igual a  0.00524
Out[ ]:
0.005243683690608274
In [ ]:
datos['Long-term fund suitability ratio (A)'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.00566
Out[ ]:
0.00566463611176392
In [ ]:
datos['Long-term fund suitability ratio (A)'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.00684
Out[ ]:
0.006847432465535919
In [ ]:
datos['Long-term fund suitability ratio (A)'].min()  # el valor minimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Long-term fund suitability ratio (A)'].max()  # el valor maximo de la caracteristica es de 1
Out[ ]:
1.0
In [ ]:
datos['Long-term fund suitability ratio (A)'].mode()   # el valor que mas se repite corresponde a 0.004716
Out[ ]:
0    0.004716
dtype: float64
In [ ]:
datos['Long-term fund suitability ratio (A)'].median()
Out[ ]:
0.00566463611176392
In [ ]:
datos['Long-term fund suitability ratio (A)'].mean()
Out[ ]:
0.008782733815036815
In [ ]:
datos['Long-term fund suitability ratio (A)'].std()
Out[ ]:
0.02815292604929068

41.Analisis Exploratorio de la Caracteristica Borrowing dependency

Índice que determina el nivel de depencia de prestamos por parte de una compañía

In [ ]:
datos['Borrowing dependency'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Borrowing dependency'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Borrowing dependency'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.37016
Out[ ]:
0.3701678435547765
In [ ]:
datos['Borrowing dependency'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a   0.372624
Out[ ]:
0.3726243225530829
In [ ]:
datos['Borrowing dependency'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.3762
Out[ ]:
0.3762707372009225
In [ ]:
datos['Borrowing dependency'].min()   # el valor minimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Borrowing dependency'].max()   # el valor mas alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Borrowing dependency'].mode()   # el valor que mas se repite coresponde a 0.369637
Out[ ]:
0    0.369637
dtype: float64
In [ ]:
datos['Borrowing dependency'].median()
Out[ ]:
0.3726243225530829
In [ ]:
datos['Borrowing dependency'].mean()   # el valor promedio corresponde a 0.3746
Out[ ]:
0.37465429459871874
In [ ]:
datos['Borrowing dependency'].std()   # los valores se alejan del promedio en 0.0162
Out[ ]:
0.016286163355500864

42.Analisis Exploratorio de la Caracteristica Contingent liabilities/Net worth

indicador del nivel que los Pasivos Contingentes representan del Patrimonio. Los grandes pasivos contingentes podrían sugerir que los compromisos de capital de una empresa aumentarán significativamente y conducirán a un deterioro de la situación financiera. Un pasivo contingente es un pasivo que puede ocurrir dependiendo del resultado de un evento futuro incierto.

In [ ]:
datos['Contingent liabilities/Net worth'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Contingent liabilities/Net worth'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Contingent liabilities/Net worth'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.005365847
Out[ ]:
0.00536584771375646
In [ ]:
datos['Contingent liabilities/Net worth'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a   0.00536584
Out[ ]:
0.00536584771375646
In [ ]:
datos['Contingent liabilities/Net worth'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.00576
Out[ ]:
0.00576435604952715
In [ ]:
datos['Contingent liabilities/Net worth'].min()   # el valor minimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Contingent liabilities/Net worth'].max()   # el valor mas alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Contingent liabilities/Net worth'].mode()   # el valor que mas se repite corresponde a 0.005366
Out[ ]:
0    0.005366
dtype: float64
In [ ]:
datos['Contingent liabilities/Net worth'].median()
Out[ ]:
0.00536584771375646
In [ ]:
datos['Contingent liabilities/Net worth'].mean()
Out[ ]:
0.005968277266479353
In [ ]:
datos['Contingent liabilities/Net worth'].std()   # los valores se alejan del promedio en 0.0121
Out[ ]:
0.012188361875858518

43.Analisis Exploratorio de la Caracteristica Operating profit/Paid-in capital

El capital pagado es la cantidad de capital "pagado" por los inversores durante la emisión de acciones ordinarias o preferentes, incluido el valor nominal de las acciones más los montos en exceso del valor nominal.

In [ ]:
datos['Operating profit/Paid-in capital'].plot(kind='box')     # se observan valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Operating profit/Paid-in capital'].plot(kind='hist')     # se observa una distribucion con inclinacion hacia la izquierda , tipo positiva
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Operating profit/Paid-in capital'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.096
Out[ ]:
0.0961046786197013
In [ ]:
datos['Operating profit/Paid-in capital'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.1041
Out[ ]:
0.10413307929063499
In [ ]:
datos['Operating profit/Paid-in capital'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.1159
Out[ ]:
0.115927337274252
In [ ]:
datos['Operating profit/Paid-in capital'].min()
Out[ ]:
0.0
In [ ]:
datos['Operating profit/Paid-in capital'].max()
Out[ ]:
1.0
In [ ]:
datos['Operating profit/Paid-in capital'].mode()
Out[ ]:
0    0.097896
dtype: float64
In [ ]:
datos['Operating profit/Paid-in capital'].median()
Out[ ]:
0.10413307929063499
In [ ]:
datos['Operating profit/Paid-in capital'].mean()
Out[ ]:
0.10897668140338525
In [ ]:
datos['Operating profit/Paid-in capital'].std()
Out[ ]:
0.0277816859856405

44.Analisis Exploratorio de la Caracteristica Net profit before tax/Paid-in capital

Net profit before tax , La ganancia antes de impuestos (PBT) es una medida de la rentabilidad de una empresa que analiza las ganancias obtenidas antes de pagar cualquier impuesto. El capital pagado, o capital aportado, es la cantidad total de efectivo u otros activos que los accionistas han dado a una empresa a cambio de acciones.

In [ ]:
datos['Net profit before tax/Paid-in capital'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Net profit before tax/Paid-in capital'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Net profit before tax/Paid-in capital'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.1693
Out[ ]:
0.16937636678983498
In [ ]:
datos['Net profit before tax/Paid-in capital'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.178
Out[ ]:
0.178455621747983
In [ ]:
datos['Net profit before tax/Paid-in capital'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.191
Out[ ]:
0.191606967800317
In [ ]:
datos['Net profit before tax/Paid-in capital'].min()  # el valor minimo es de 0
Out[ ]:
0.0
In [ ]:
datos['Net profit before tax/Paid-in capital'].max()  # el valor mas alto es de 1
Out[ ]:
1.0
In [ ]:
datos['Net profit before tax/Paid-in capital'].mode()   # el valor que mas se repite es 0.178
Out[ ]:
0    0.178441
dtype: float64
In [ ]:
datos['Net profit before tax/Paid-in capital'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.178
Out[ ]:
0.178455621747983
In [ ]:
datos['Net profit before tax/Paid-in capital'].mean()  # el promedio es de 0.1827
Out[ ]:
0.18271502907673617
In [ ]:
datos['Net profit before tax/Paid-in capital'].std()   # los valors se alejan del promedio 0.0307
Out[ ]:
0.03078477150830977

45.Analisis Exploratorio de la Caracteristica Inventory and accounts receivable/Net value

La valoración de los inventarios y cuentas por cobrar, al valor neto implica, el valor en términos de la cantidad que recibiría en el momento de la venta, menos los costos de venta. Esta valoración se utiliza como un monto más real al valor de mercado que se puede obtener por la venta de un activo.

In [ ]:
datos['Inventory and accounts receivable/Net value'].plot(kind='box')    # se observan valores atipicos
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Inventory and accounts receivable/Net value'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Inventory and accounts receivable/Net value'].quantile(0.25)  # indica que el 25 % de las empresas presentan un valor menor o igual a  0.397
Out[ ]:
0.3974026791778925
In [ ]:
datos['Inventory and accounts receivable/Net value'].quantile(0.5) # indica que el 50% de las empresas presentan un valor menor o igual a  0.40013
Out[ ]:
0.40013102490143
In [ ]:
datos['Inventory and accounts receivable/Net value'].quantile(0.75) # indica que el 75% de las empresas presentan un valor menor o igual a 0.4045
Out[ ]:
0.40455077080958096
In [ ]:
datos['Inventory and accounts receivable/Net value'].min()  # el valor minimo corresponde a cero
Out[ ]:
0.0
In [ ]:
datos['Inventory and accounts receivable/Net value'].max()   # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['Inventory and accounts receivable/Net value'].mode()   # el valor que mas se repite corresponde a 0.393663
Out[ ]:
0    0.393663
dtype: float64
In [ ]:
datos['Inventory and accounts receivable/Net value'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.40013
Out[ ]:
0.40013102490143
In [ ]:
datos['Inventory and accounts receivable/Net value'].mean()  # el valor promedio es 0.4024
Out[ ]:
0.40245933052066785
In [ ]:
datos['Inventory and accounts receivable/Net value'].std()  # los valores se alejan en 0.013 del promedio
Out[ ]:
0.013324079587932258

46.Analisis Exploratorio de la Caracteristica Total Asset Turnover

Rotación de Activos totales: Mide el valor de las ventas o los ingresos de una empresa en relación con el valor de sus activos. El índice de rotación de activos se puede utilizar como indicador de la eficiencia con la que una empresa utiliza sus activos para generar ingresos.

In [ ]:
datos['Total Asset Turnover'].plot(kind='box')    # se observa valores atipicos
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Total Asset Turnover'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})               # distribucion del tipo positiva 
plt.show()
In [ ]:
datos['Total Asset Turnover'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.076
Out[ ]:
0.0764617691154423
In [ ]:
datos['Total Asset Turnover'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a   0.1184
Out[ ]:
0.11844077961019502
In [ ]:
datos['Total Asset Turnover'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.176
Out[ ]:
0.17691154422788602
In [ ]:
datos['Total Asset Turnover'].min()   # el valor minimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Total Asset Turnover'].max()   # el valormaximo corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Total Asset Turnover'].mode()    # el valor que mas se repite es 0.07946
Out[ ]:
0    0.07946
dtype: float64
In [ ]:
datos['Total Asset Turnover'].median()   # el promedio de la caracteristica total asset turnover es 0.1184
Out[ ]:
0.11844077961019502
In [ ]:
datos['Total Asset Turnover'].std()   # indica la distancia que se alejan del promedio
Out[ ]:
0.10114496849292293

47.Analisis Exploratorio de la Caracteristica Accounts Receivable Turnover

La rotación de las cuentas por cobrar se describe como una proporción de las cuentas por cobrar promedio para un período dividido por las ventas netas a crédito para ese mismo período. Esta relación le da a la empresa una idea sólida de la eficiencia con la que recauda las deudas contraídas con el crédito que extendió, y un número menor muestra una mayor eficiencia.

In [ ]:
datos['Accounts Receivable Turnover'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Accounts Receivable Turnover'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Accounts Receivable Turnover'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a 0.00071013
Out[ ]:
0.000710133606565692
In [ ]:
datos['Accounts Receivable Turnover'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.000967
Out[ ]:
0.000967810658090958
In [ ]:
datos['Accounts Receivable Turnover'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.00145
Out[ ]:
0.00145475941687886
In [ ]:
datos['Accounts Receivable Turnover'].min()   # el valor minimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Accounts Receivable Turnover'].max()   # el valor maximo corresponde a 9740000000
Out[ ]:
9740000000.0
In [ ]:
datos['Accounts Receivable Turnover'].mode()  #  multimodal presenta varios valores que se repiten mas
Out[ ]:
0    0.000712
1    0.000716
2    0.000808
dtype: float64
In [ ]:
datos['Accounts Receivable Turnover'].median()
Out[ ]:
0.000967810658090958
In [ ]:
datos['Accounts Receivable Turnover'].mean()
Out[ ]:
12789705.237553565
In [ ]:
datos['Accounts Receivable Turnover'].std()   # los valores se alejan del promedio  278259836
Out[ ]:
278259836.9840534

48.Analisis Exploratorio de la Caracteristica Average Collection Days

El período de cobranza promedio se calcula dividiendo el saldo promedio de las cuentas por cobrar por el total de ventas netas a crédito del período y multiplicando el cociente por el número de días del período.

In [ ]:
datos['Average Collection Days'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Average Collection Days'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Average Collection Days'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.00438
Out[ ]:
0.00438653043972043
In [ ]:
datos['Average Collection Days'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a   0.0065
Out[ ]:
0.00657253743323499
In [ ]:
datos['Average Collection Days'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.00897
Out[ ]:
0.008972875581191801
In [ ]:
datos['Average Collection Days'].min()   # el valor mas bajo es 0
Out[ ]:
0.0
In [ ]:
datos['Average Collection Days'].max()   # el valor mas alto 9730000000
Out[ ]:
9730000000.0
In [ ]:
datos['Average Collection Days'].mode()   # el valor que mas se repite 0
Out[ ]:
0    0.0
dtype: float64
In [ ]:
datos['Average Collection Days'].median()
Out[ ]:
0.00657253743323499
In [ ]:
datos['Average Collection Days'].mean()    # el valor del promedio es 9826220
Out[ ]:
9826220.861191586
In [ ]:
datos['Average Collection Days'].std()  # los valores se alejan del promedio en 256358895
Out[ ]:
256358895.70533204

49.Analisis Exploratorio de la Caracteristica Inventory Turnover Rate (times)

La rotación de inventario es una proporción que mide la cantidad de veces que se vende o consume el inventario en un período de tiempo determinado. También conocida como rotación de inventario, rotación de stock y rotación de existencias, la fórmula de rotación de inventario se calcula dividiendo el costo de los bienes vendidos

In [ ]:
datos['Inventory Turnover Rate (times)'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Inventory Turnover Rate (times)'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Inventory Turnover Rate (times)'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.00017
Out[ ]:
0.0001728255554827355
In [ ]:
datos['Inventory Turnover Rate (times)'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.00076
Out[ ]:
0.000764674265386299
In [ ]:
datos['Inventory Turnover Rate (times)'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  4620000000
Out[ ]:
4620000000.0
In [ ]:
datos['Inventory Turnover Rate (times)'].min()   # el valor minimo es 0
Out[ ]:
0.0
In [ ]:
datos['Inventory Turnover Rate (times)'].max()    # el valor mas alto es 9990000000
Out[ ]:
9990000000.0
In [ ]:
datos['Inventory Turnover Rate (times)'].mode()  # el valor que mas se repite corresponde a 19100000
Out[ ]:
0    19100000.0
dtype: float64
In [ ]:
datos['Inventory Turnover Rate (times)'].median()
Out[ ]:
0.000764674265386299
In [ ]:
datos['Inventory Turnover Rate (times)'].mean()
Out[ ]:
2149106056.60753
In [ ]:
datos['Inventory Turnover Rate (times)'].std()   # los valores se alejan del promedio 3247967014
Out[ ]:
3247967014.0479045

50.Analisis Exploratorio de la Caracteristica Fixed Assets Turnover Frequency

La fórmula del índice de rotación de activos fijos se calcula dividiendo las ventas netas por la propiedad, planta y equipo total neto de la depreciación acumulada. Como puede ver, es una ecuación bastante simple.

In [ ]:
datos['Fixed Assets Turnover Frequency'].plot(kind='box')   # se observan valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Fixed Assets Turnover Frequency'].plot(kind='hist')      # distribucion tipo positiva 
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Fixed Assets Turnover Frequency'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.000233
Out[ ]:
0.000233001306471611
In [ ]:
datos['Fixed Assets Turnover Frequency'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.000593
Out[ ]:
0.000593094234655011
In [ ]:
datos['Fixed Assets Turnover Frequency'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.00365
Out[ ]:
0.00365237112871734
In [ ]:
datos['Fixed Assets Turnover Frequency'].min()  # el valor mas bajoes 0
Out[ ]:
0.0
In [ ]:
datos['Fixed Assets Turnover Frequency'].max()   # el valor mas alto corresponde a 9990000000
Out[ ]:
9990000000.0
In [ ]:
datos['Fixed Assets Turnover Frequency'].mode()   # el valor que más se repite es 0.000102
Out[ ]:
0    0.000102
dtype: float64
In [ ]:
datos['Fixed Assets Turnover Frequency'].median()
Out[ ]:
0.000593094234655011
In [ ]:
datos['Fixed Assets Turnover Frequency'].mean()    # el valor  promedio corresponde  a 1008595981
Out[ ]:
1008595981.8174767
In [ ]:
datos['Fixed Assets Turnover Frequency'].std()
Out[ ]:
2477557316.9201717

51.Analisis Exploratorio de la Caracteristica Net Worth Turnover Rate (times)

Es una relación que compara el agotamiento del capital de trabajo ... del tiempo necesario para convertir los activos y pasivos corrientes netos en efectivo.

In [ ]:
datos['Net Worth Turnover Rate (times)'].plot(kind='box')    # se observan valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Net Worth Turnover Rate (times)'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Net Worth Turnover Rate (times)'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.02177
Out[ ]:
0.0217741935483871
In [ ]:
datos['Net Worth Turnover Rate (times)'].quantile(0.5)  # indica que el  50% de las empresas presentan un valor menor o igual a  0.0295
Out[ ]:
0.0295161290322581
In [ ]:
datos['Net Worth Turnover Rate (times)'].quantile(0.75) # indica que el 75% de las empresas presentan un valor menor o igual a  0.042
Out[ ]:
0.0429032258064516
In [ ]:
datos['Net Worth Turnover Rate (times)'].min()   # el valor minimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Net Worth Turnover Rate (times)'].max()   # el valor mas alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Net Worth Turnover Rate (times)'].mode()   # el valor que mas se repite 0.028387
Out[ ]:
0    0.028387
dtype: float64
In [ ]:
datos['Net Worth Turnover Rate (times)'].median()
Out[ ]:
0.0295161290322581
In [ ]:
datos['Net Worth Turnover Rate (times)'].mean()   # el promedio es de 0.038595
Out[ ]:
0.03859505461495215
In [ ]:
datos['Net Worth Turnover Rate (times)'].std()    # los valores se alejan del promedio e 0.0366
Out[ ]:
0.03668034356041346

52.Analisis Exploratorio de la Caracteristica Revenue per person

Son los ingresos o las ventas totales que realiza una empresa divididos por las personas a tiempo completo que trabajan allí. Esta relación se encuentra entre las más universalmente aplicables y se utiliza a menudo para comparar empresas dentro de la misma industria.

In [ ]:
datos['Revenue per person'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Revenue per person'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Revenue per person'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a   0.0104
Out[ ]:
0.010432854016421151
In [ ]:
datos['Revenue per person'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a 0.0186155
Out[ ]:
0.0186155134174464
In [ ]:
datos['Revenue per person'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a   0.03585
Out[ ]:
0.0358547655068079
In [ ]:
datos['Revenue per person'].min()   # el valor minimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Revenue per person'].max()   # el valor mas alto en la caracterisca es 881000000
Out[ ]:
8810000000.0
In [ ]:
datos['Revenue per person'].mode()   # el valor que más se repite es 0.01361
Out[ ]:
0    0.01361
dtype: float64
In [ ]:
datos['Revenue per person'].median()   # la mediana  indica que el 50% de las empresas presentan un valor menor o igual a 0.0186 en revenue per person
Out[ ]:
0.0186155134174464
In [ ]:
datos['Revenue per person'].mean()   # el valor promedio de la Revenue per person es de  2325854
Out[ ]:
2325854.266358269
In [ ]:
datos['Revenue per person'].std()    # los valores se alejan del promedio en   1366326554
Out[ ]:
136632654.38993618

53.Analisis Exploratorio de la Caracteristica Operating profit per person

El beneficio por empleado, también conocido como ingreso neto por empleado (NIPE), es una métrica que puede utilizar para calcular el ingreso neto de su empresa dividido por el número total de empleados.

In [ ]:
datos['Operating profit per person'].plot(kind='box')    # se observan valores atipicos, una mediana cercana a  0.4
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Operating profit per person'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Operating profit per person'].quantile(0.25)  # indica que el25 % de las empresas presentan un valor menor o igual a  03924
Out[ ]:
0.392437981954275
In [ ]:
datos['Operating profit per person'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.3958
Out[ ]:
0.395897876574478
In [ ]:
datos['Operating profit per person'].quantile(0.75)  # indica que el75% de las empresas presentan un valor menor o igual a  0.4018
Out[ ]:
0.40185093055335697
In [ ]:
datos['Operating profit per person'].min()   # el valor minimo coresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Operating profit per person'].max()   # el valor mas alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Operating profit per person'].mode()   # el valor que mas se repite corresponde a 0.394
Out[ ]:
0    0.394462
dtype: float64
In [ ]:
datos['Operating profit per person'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a   0.39589
Out[ ]:
0.395897876574478
In [ ]:
datos['Operating profit per person'].mean()  # el valor promedio correspodne a 0.40067
Out[ ]:
0.4006710150813351
In [ ]:
datos['Operating profit per person'].std()   # los valores se alejan del promedio en 0.032
Out[ ]:
0.0327201441946995

54.Analisis Exploratorio de la Caracteristica Allocation rate per person

Una tasa de asignación es un porcentaje del efectivo o desembolso de capital de un inversionista que se destina a una inversión final. La tasa de asignación se refiere con mayor frecuencia a la cantidad de capital invertido en un producto neto de cualquier tarifa en la que se pueda incurrir a través de la transacción de inversión.

In [ ]:
datos['Allocation rate per person'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Allocation rate per person'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Allocation rate per person'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.0041
Out[ ]:
0.00412052899796365
In [ ]:
datos['Allocation rate per person'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.0078
Out[ ]:
0.00784437335865574
In [ ]:
datos['Allocation rate per person'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.015
Out[ ]:
0.015020308976719
In [ ]:
datos['Allocation rate per person'].min()  # el valor minimo 0
Out[ ]:
0.0
In [ ]:
datos['Allocation rate per person'].max()  # el valor maximo corresponde a 9570000000
Out[ ]:
9570000000.0
In [ ]:
datos['Allocation rate per person'].mode()   # el valor que mas se repite corresponde  a 0
Out[ ]:
0    0.0
dtype: float64
In [ ]:
datos['Allocation rate per person'].median()  # la mediana  indica que el 50% de las empresas presentan un valor menor o igual a 0.007844 en Allocation rate per pseron
Out[ ]:
0.00784437335865574
In [ ]:
datos['Allocation rate per person'].mean()   # el valor promedio corresponde 11255785 en allocation rate per person
Out[ ]:
11255785.321742103
In [ ]:
datos['Allocation rate per person'].std()   # los valores se desvian del promedio en 294506294
Out[ ]:
294506294.1167716

55.Analisis Exploratorio de la Caracteristica Working Capital to Total Assets

Mide la capacidad de una empresa para cubrir sus obligaciones financieras a corto plazo (pasivos corrientes totales) comparando sus activos corrientes totales con sus activos totales. Esta relación puede proporcionar una idea de la liquidez de la empresa.

In [ ]:
datos['Working Capital to Total Assets'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Working Capital to Total Assets'].plot(kind='hist')    # distribucion con inclinacion hacia la derecha, tipo negativa 
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Working Capital to Total Assets'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.7743
Out[ ]:
0.774308962762401
In [ ]:
datos['Working Capital to Total Assets'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.8102
Out[ ]:
0.8102752289846601
In [ ]:
datos['Working Capital to Total Assets'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a  0.8503
Out[ ]:
0.8503828485419614
In [ ]:
datos['Working Capital to Total Assets'].min()  # el valorminimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Working Capital to Total Assets'].max()   # el valor maximo corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Working Capital to Total Assets'].mode()   # multimodal , varios valores que se repiten varias veces
Out[ ]:
0       0.000000
1       0.475181
2       0.494210
3       0.588283
4       0.588815
          ...   
6814    0.986300
6815    0.988461
6816    0.989152
6817    0.989252
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Working Capital to Total Assets'].median()    # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.8102
Out[ ]:
0.8102752289846601
In [ ]:
datos['Working Capital to Total Assets'].mean()   # el valor promedio es de 0.814
Out[ ]:
0.8141251702613322
In [ ]:
datos['Working Capital to Total Assets'].std()   # los valores se alejan del promedio en 0.0590
Out[ ]:
0.059054402648263365

56.Analisis Exploratorio de la Caracteristica Quick Assets/Total Assets

Brinda la información de la participación o peso relativo de los activos circulantes entre el total de los activos. Los QUICK ASSETS se refieren a los activos propiedad de una empresa con un valor comercial o de cambio que se pueden convertir fácilmente en efectivo o que ya están en forma de efectivo.

In [ ]:
datos['Quick Assets/Total Assets'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Quick Assets/Total Assets'].plot(kind='hist')     # distribucion con tipo inclinacion hacia la izquierda, tipo positiva
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Quick Assets/Total Assets'].quantile(0.25)    # indica que el 25% de las empresas presentan un valor menor o igual a 0.24197
Out[ ]:
0.24197285659393997
In [ ]:
datos['Quick Assets/Total Assets'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a 0.38645
Out[ ]:
0.386450924981744
In [ ]:
datos['Quick Assets/Total Assets'].quantile(0.75)  # indica que el75 % de las empresas presentan un valor menor o igual a   0.54059
Out[ ]:
0.540593673285078
In [ ]:
datos['Quick Assets/Total Assets'].min()  # valor mas bajo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Quick Assets/Total Assets'].max()  # el valos mas alto corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Quick Assets/Total Assets'].mode()   # multimodal ,
Out[ ]:
0       0.000000
1       0.001469
2       0.006131
3       0.006310
4       0.011524
          ...   
6814    0.965243
6815    0.978981
6816    0.981009
6817    0.988944
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Quick Assets/Total Assets'].median()  # la mediana  indica que el 50% de las empresas presentan un valor menor o igual a 0.3864
Out[ ]:
0.386450924981744
In [ ]:
datos['Quick Assets/Total Assets'].mean()
Out[ ]:
0.40013181236505724
In [ ]:
datos['Quick Assets/Total Assets'].std()   # los valores se desvian del promedio en 0.2019
Out[ ]:
0.2019980666806821

57.Analisis Exploratorio de la Caracteristica Current Assets/Total Assets

Nos brinda el peso de los Activos Circulantes entre el Total de Activos. El activo corriente total es la suma de todos los activos corrientes. Estos son efectivo, equivalentes de efectivo, gastos pagados por adelantado, inventario o cualquier otro activo que se espera que se convierta en efectivo durante el próximo año.

In [ ]:
datos['Current Assets/Total Assets'].plot(kind='box')     # esta distribucion no se observan valores atipicos,  la mediana está cercana a 0.5
sns.set(rc={'figure.figsize':(7,7)})                      # el valor minimo correspnde a 0
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Current Assets/Total Assets'].plot(kind='hist')    # una distribucion multimodal , 
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Current Assets/Total Assets'].quantile(0.25)  #  # indica que el 25% de las empresas presentan un valor menor o igual a 0.3528 en Current Assets/ total assets
Out[ ]:
0.35284541721511353
In [ ]:
datos['Current Assets/Total Assets'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a 0.68905
Out[ ]:
0.514829793890847
In [ ]:
datos['Current Assets/Total Assets'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a 0.68905
Out[ ]:
0.6890506806831516
In [ ]:
datos['Current Assets/Total Assets'].min()      # el valor minimo es cero
Out[ ]:
0.0
In [ ]:
datos['Current Assets/Total Assets'].max()   # el valor maximo es uno
Out[ ]:
1.0
In [ ]:
datos['Current Assets/Total Assets'].mode()   # una distribucion multimodal, varios numeros son los que tinen mayor veces se repiten
Out[ ]:
0       0.000000
1       0.001407
2       0.006045
3       0.020835
4       0.020920
          ...   
6814    0.991139
6815    0.995453
6816    0.996105
6817    0.998800
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Current Assets/Total Assets'].median()   # la mediana  indica que el 50% de las empresas presentan un valor menor o igual a 0.5148
Out[ ]:
0.514829793890847
In [ ]:
datos['Current Assets/Total Assets'].mean()  # el valor promedio de la Current Assets / total assets  es de 0.5222
Out[ ]:
0.5222734467680333
In [ ]:
datos['Current Assets/Total Assets'].std()   # los valores se alejan del promedio en 0.21811
Out[ ]:
0.21811182151419325

58.Analisis Exploratorio de la Caracteristica Cash/Total Assets

Efectivo / Activos totales: mide la parte de los activos de una empresa que se mantiene en efectivo o valores negociables. Aunque una proporción alta puede indicar cierto grado de seguridad desde el punto de vista del acreedor, las cantidades excesivas de efectivo pueden considerarse ineficientes.

In [ ]:
datos['Cash/Total Assets'].plot(kind='box')   # se observan valores atipicos, la mayor concentracion de valors entre 0 y 0.2
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Cash/Total Assets'].plot(kind='hist')      # una distribucion del tipo positiva , concetracion de datos hacia la izquierda
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Cash/Total Assets'].quantile(0.25)   #  indica que el 25% de las empresas presentan un valor menor o igual a 0.03354
Out[ ]:
0.03354322123979425
In [ ]:
datos['Cash/Total Assets'].quantile(0.5)    # indica que el 50% de las empresas presentan un valor menor o igual a 0.07488
Out[ ]:
0.0748874639354301
In [ ]:
datos['Cash/Total Assets'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a 0.15107
Out[ ]:
0.1610731518633315
In [ ]:
datos['Cash/Total Assets'].min()  # el valor mas bajo es cero
Out[ ]:
0.0
In [ ]:
datos['Cash/Total Assets'].max()   # el valor mas alto es de 1
Out[ ]:
1.0
In [ ]:
datos['Cash/Total Assets'].mode()   # distribucon del tipo multimodal, varios numeros tienen la caracteristica de ser la moda , hay varios numeros que se repiten mucho
Out[ ]:
0       0.000000
1       0.000184
2       0.000315
3       0.000379
4       0.000409
          ...   
6814    0.898929
6815    0.903032
6816    0.916271
6817    0.925018
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Cash/Total Assets'].median()   # la mediana indica que el 50% de las empresas presentan un valor menor o igual a 0.074
Out[ ]:
0.0748874639354301
In [ ]:
datos['Cash/Total Assets'].mean()  # el promedio de cash/total assets es de 0.12409
Out[ ]:
0.12409456048965258
In [ ]:
datos['Cash/Total Assets'].std()   #  los valores se alejan del promedio en 0.1392
Out[ ]:
0.13925058358332654

59.Analisis Exploratorio de la Caracteristica Quick Assets/Current Liability

La razón corriente mide la capacidad de una empresa para pagar los pasivos corrientes o de corto plazo (deuda y cuentas por pagar) con sus activos corrientes o de corto plazo (efectivo, inventario y cuentas por cobrar).

In [ ]:
datos['Quick Assets/Current Liability'].plot(kind='box')    # valors atipicos, valors muy dispersos esta caracteristica, el valor minimo es cero y el valor maximo 
sns.set(rc={'figure.figsize':(7,7)})                        # corresponde a 8820000000
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Quick Assets/Current Liability'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Quick Assets/Current Liability'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a 0.00523
Out[ ]:
0.005239775826640915
In [ ]:
datos['Quick Assets/Current Liability'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a 0.0079
Out[ ]:
0.00790889798045124
In [ ]:
datos['Quick Assets/Current Liability'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a 0.0129
Out[ ]:
0.0129509103075746
In [ ]:
datos['Quick Assets/Current Liability'].min()
Out[ ]:
0.0
In [ ]:
datos['Quick Assets/Current Liability'].max()
Out[ ]:
8820000000.0
In [ ]:
datos['Quick Assets/Current Liability'].mode()   # multimodal, varios numeros que se repiten mucho
Out[ ]:
0       0.000000e+00
1       1.199931e-04
2       1.204469e-04
3       1.319398e-04
4       1.354058e-04
            ...     
6814    3.251893e-01
6815    1.000000e+00
6816    7.540000e+09
6817    8.140000e+09
6818    8.820000e+09
Length: 6819, dtype: float64
In [ ]:
datos['Quick Assets/Current Liability'].median()  # la mediana  indica que el 50% de las empresas presentan un valor menor o igual a 0.0079
Out[ ]:
0.00790889798045124
In [ ]:
datos['Quick Assets/Current Liability'].mean()   # el valor promedio de Quick assets / current liabillity es de 3592902
Out[ ]:
3592902.1968296594
In [ ]:
datos['Quick Assets/Current Liability'].std()    # una desviacion estandar muy alta, demuestra lo dispersos que son los valores en esta caracteristica. 
                                                  # los valores se alejan del promedio en 171620908
Out[ ]:
171620908.60682204

60.Analisis Exploratorio de la Caracteristica Cash/Current Liability

La capacidad de una empresa para liquidar sus pasivos corrientes utilizando solo su efectivo e inversiones de alta liquidez

In [ ]:
datos['Cash/Current Liability'].plot(kind='box')    # se observan valores atipicos , valor minimo de cero, y la mediana cercana a 0.0049
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Cash/Current Liability'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Cash/Current Liability'].quantile(0.25)    # indica que el 25% de las empresas presentan un valor menor o igual a 0.0019
Out[ ]:
0.001973007541548905
In [ ]:
datos['Cash/Current Liability'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a 0.0049
Out[ ]:
0.0049038864700734295
In [ ]:
datos['Cash/Current Liability'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a 0.0128
Out[ ]:
0.0128055731079178
In [ ]:
datos['Cash/Current Liability'].min()    # el valor minimo es cero
Out[ ]:
0.0
In [ ]:
datos['Cash/Current Liability'].max()   # el valor maximo es de 9650000000
Out[ ]:
9650000000.0
In [ ]:
datos['Cash/Current Liability'].mode()   # caracteristica multimodal, tiene tres valores que se repiten mucho
Out[ ]:
0    4.610000e+09
1    7.510000e+09
2    8.870000e+09
dtype: float64
In [ ]:
datos['Cash/Current Liability'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a 0.0049
Out[ ]:
0.0049038864700734295
In [ ]:
datos['Cash/Current Liability'].mean()  # el valor promedio de la caracteristica es 37159994
Out[ ]:
37159994.14713339
In [ ]:
datos['Cash/Current Liability'].std()   # los valores se alejan del promedio en 510350903
Out[ ]:
510350903.16273266

61.Analisis Exploratorio de la Caracteristica Current Liability to Assets

La razón corriente compara todos los activos corrientes de una empresa con sus pasivos corrientes. Por lo general, se definen como activos que son efectivo o que se convertirán en efectivo en un año o menos, y pasivos que se pagarán en un año o menos.

In [ ]:
datos['Current Liability to Assets'].plot(kind='box')   # se observan valores atpicos, la mediana cercana a 0.08, el valor minimo es cero
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Current Liability to Assets'].plot(kind='hist')     # distribucion del tipo positiva, valores acumulados hacia la izquierda
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Current Liability to Assets'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a 0.053
Out[ ]:
0.0533012764320206
In [ ]:
datos['Current Liability to Assets'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a  0.082
Out[ ]:
0.0827047949822228
In [ ]:
datos['Current Liability to Assets'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a   0.1195
Out[ ]:
0.1195229934695275
In [ ]:
datos['Current Liability to Assets'].min()   # el valormas bajo es cero
Out[ ]:
0.0
In [ ]:
datos['Current Liability to Assets'].max()   # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['Current Liability to Assets'].mode()   # distribucion multimodal, varios numeros que se repiten mucho
Out[ ]:
0       0.000000
1       0.000784
2       0.000847
3       0.001043
4       0.001481
          ...   
6814    0.307686
6815    0.323917
6816    0.336844
6817    0.343143
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Current Liability to Assets'].median()  #   indica que el 50% de las empresas presentan un valor menor o igual a 0.082
Out[ ]:
0.0827047949822228
In [ ]:
datos['Current Liability to Assets'].mean()  # el valor del promedio de la caracteristica es 0.096
Out[ ]:
0.09067279456762385
In [ ]:
datos['Current Liability to Assets'].std()  # los valores se desvian del promedio en 0.0502
Out[ ]:
0.05028985666891828

62.Analisis Exploratorio de la Caracteristica Operating Funds to Liability

¿Qué son las operaciones de financiación? El término operaciones de financiación se refiere a la conversión de deuda a corto plazo en deuda a largo plazo. Este proceso lo utilizan a menudo las corporaciones junto con los gobiernos para convertir bonos a corto plazo en tenencias de bonos a largo plazo.

In [ ]:
datos['Operating Funds to Liability'].plot(kind='box')    # se observan valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Operating Funds to Liability'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Operating Funds to Liability'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a   0.3410
Out[ ]:
0.3410229773557805
In [ ]:
datos['Operating Funds to Liability'].quantile(0.5)    # indica que el 50 % de las empresas presentan un valor menor o igual a   0.3485
Out[ ]:
0.34859665710613696
In [ ]:
datos['Operating Funds to Liability'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a 0.360914
Out[ ]:
0.3609148870133705
In [ ]:
datos['Operating Funds to Liability'].min()   # el valor mas bajo es cero
Out[ ]:
0.0
In [ ]:
datos['Operating Funds to Liability'].max()  # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['Operating Funds to Liability'].mode()  # dsitribucion multimodal , varios numeros que se repiten mucho
Out[ ]:
0       0.000000
1       0.014723
2       0.026274
3       0.086267
4       0.087098
          ...   
6814    0.756949
6815    0.776297
6816    0.835717
6817    0.956425
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Operating Funds to Liability'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a 0.3485
Out[ ]:
0.34859665710613696
In [ ]:
datos['Operating Funds to Liability'].mean()  # el valor del promedio es 0.3582
Out[ ]:
0.3538280041215869
In [ ]:
datos['Operating Funds to Liability'].std()   # los valores se alejan del promedio en 0.03514
Out[ ]:
0.03514718417918804

63.Analisis Exploratorio de la Caracteristica Inventory/Working Capital

El inventario al capital de trabajo es un índice de liquidez que mide la cantidad de capital de trabajo que está inmovilizado en el inventario.

In [ ]:
datos['Inventory/Working Capital'].plot(kind='box')   # se observan valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Inventory/Working Capital'].plot(kind='hist')     # la mayoria de los datos se acumulan cerca de 0.27 y 0.28
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Inventory/Working Capital'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a 0.277033
Out[ ]:
0.2770339694810945
In [ ]:
datos['Inventory/Working Capital'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a  0.277177
Out[ ]:
0.277177699032242
In [ ]:
datos['Inventory/Working Capital'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a  0.2774
Out[ ]:
0.2774287054274715
In [ ]:
datos['Inventory/Working Capital'].min()   # el valor minimo corresponde a 0
Out[ ]:
0.0
In [ ]:
datos['Inventory/Working Capital'].max()   # el valor maximo corresponde a 1
Out[ ]:
1.0
In [ ]:
datos['Inventory/Working Capital'].mode()  # el valor que mas se repite es 0.2769
Out[ ]:
0    0.276975
dtype: float64
In [ ]:
datos['Inventory/Working Capital'].median()  # indica que el 50% de las empresas presentan un valor menor o igual a  0.2771
Out[ ]:
0.277177699032242
In [ ]:
datos['Inventory/Working Capital'].mean()  # el valor promedio es 0.2773
Out[ ]:
0.27739510610233076
In [ ]:
datos['Inventory/Working Capital'].std()  # los valores se alejan del promedio en 0.01046
Out[ ]:
0.010468846972945247

64.Analisis Exploratorio de la Caracteristica Inventory/Current Liability

Una proporción rápida baja indica que una organización puede tardar en pagar sus obligaciones. Esta relación calcula cuántos días le toma a la organización recibir el pago de sus clientes.

In [ ]:
datos['Inventory/Current Liability'].plot(kind='box')     # se observan valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Inventory/Current Liability'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Inventory/Current Liability'].quantile(0.25)   # indica que el 25 % de las empresas presentan un valor menor o igual a 0.003163
Out[ ]:
0.00316314767469916
In [ ]:
datos['Inventory/Current Liability'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a 0.0064
Out[ ]:
0.00649733535347341
In [ ]:
datos['Inventory/Current Liability'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a 0.0111
Out[ ]:
0.011146766748190151
In [ ]:
datos['Inventory/Current Liability'].min()  # el valor minimo es cero
Out[ ]:
0.0
In [ ]:
datos['Inventory/Current Liability'].max() # el valor maximo es 9910000000
Out[ ]:
9910000000.0
In [ ]:
datos['Inventory/Current Liability'].mode()   # el valor que mas se repite es cero
Out[ ]:
0    0.0
dtype: float64
In [ ]:
datos['Inventory/Current Liability'].median()
Out[ ]:
0.00649733535347341
In [ ]:
datos['Inventory/Current Liability'].mean()  # el valor promedio es 55806804
Out[ ]:
55806804.52577965
In [ ]:
datos['Inventory/Current Liability'].std()  # los valores se alejan del promedio en 582051554
Out[ ]:
582051554.6194199

65.Analisis Exploratorio de la Caracteristica Current Liabilities/Liability

La razón corriente mide la capacidad de una empresa para pagar sus deudas u obligaciones financieras a corto plazo.

In [ ]:
datos['Current Liabilities/Liability'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Current Liabilities/Liability'].plot(kind='hist')    # distribucion negativa, valores acumulados hacia la derecha
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Current Liabilities/Liability'].quantile(0.25)  # indica que el  25% de las empresas presentan un valor menor o igual a   0.6269
Out[ ]:
0.6269807662218725
In [ ]:
datos['Current Liabilities/Liability'].quantile(0.5)  # indica que el  50% de las empresas presentan un valor menor o igual a 0.8068
Out[ ]:
0.8068814047133329
In [ ]:
datos['Current Liabilities/Liability'].quantile(0.75)  # indica que el 75 % de las empresas presentan un valor menor o igual a 0.94202
Out[ ]:
0.9420266937000692
In [ ]:
datos['Current Liabilities/Liability'].min()   # el valor minimo es cero
Out[ ]:
0.0
In [ ]:
datos['Current Liabilities/Liability'].max()   # el valor maximo es uno
Out[ ]:
1.0
In [ ]:
datos['Current Liabilities/Liability'].mode()  # el valor que mas se repite es 1
Out[ ]:
0    1.0
dtype: float64
In [ ]:
datos['Current Liabilities/Liability'].median()  # indica que el 50% de las empresas presentan un valor menor o igual a  0.8068
Out[ ]:
0.8068814047133329
In [ ]:
datos['Current Liabilities/Liability'].mean()  # el promedio es 0.7615
Out[ ]:
0.761598877585336
In [ ]:
datos['Current Liabilities/Liability'].std()  # los valores se alejan del promedio en 0.2066
Out[ ]:
0.20667676768344168

66.Analisis Exploratorio de la Caracteristica Working Capital/Equity

¿Qué es el capital de trabajo? El capital de trabajo, también conocido como capital de trabajo neto (NWC), es la diferencia entre los activos corrientes de una empresa (efectivo, cuentas por cobrar / facturas impagas de los clientes, inventarios de materias primas y productos terminados) y sus pasivos corrientes, como cuentas por pagar y deudas.

In [ ]:
datos['Working Capital/Equity'].plot(kind='box')    # se observan valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Working Capital/Equity'].plot(kind='hist')     # valores concentados entre 0.7 y 0.8
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Working Capital/Equity'].quantile(0.25)    # indica que el   25 % de las empresas presentan un valor menor o igual a  0.733
Out[ ]:
0.7336118185643419
In [ ]:
datos['Working Capital/Equity'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a   0.73601
Out[ ]:
0.736012732265696
In [ ]:
datos['Working Capital/Equity'].quantile(0.75)   # indica que el  75 % de las empresas presentan un valor menor o igual a   0.7385
Out[ ]:
0.738559910578823
In [ ]:
datos['Working Capital/Equity'].min()   # el valor minimo corresponde a cero
Out[ ]:
0.0
In [ ]:
datos['Working Capital/Equity'].max()   # el valor más alto en la caracteristica es 1
Out[ ]:
1.0
In [ ]:
datos['Working Capital/Equity'].mode()  # distribucion multimodal , varios valores que se repiten mucho
Out[ ]:
0       0.000000
1       0.507149
2       0.517571
3       0.612578
4       0.645339
          ...   
6814    0.782322
6815    0.824317
6816    0.825197
6817    0.961070
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Working Capital/Equity'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.73601
Out[ ]:
0.736012732265696
In [ ]:
datos['Working Capital/Equity'].mean()  # el promedio del working capital / equity  es de 0.7358
Out[ ]:
0.7358165257322186
In [ ]:
datos['Working Capital/Equity'].std()   # los valores se desvian del promedio en 0.01167
Out[ ]:
0.011678026475599061

67.Analisis Exploratorio de la Caracteristica Current Liabilities/Equity

El ratio compara los pasivos circulantes de una empresa con el capital contable y se puede utilizar para evaluar cuánto apalancamiento está utilizando una empresa.

In [ ]:
datos['Current Liabilities/Equity'].plot(kind='box')   # se observan valores atipicos y el valor minimo igual a cero, con una mediana cercana a 0.32
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Current Liabilities/Equity'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Current Liabilities/Equity'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.3280
Out[ ]:
0.328095841686878
In [ ]:
datos['Current Liabilities/Equity'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.3296
Out[ ]:
0.32968513313592895
In [ ]:
datos['Current Liabilities/Equity'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a  0.3323
Out[ ]:
0.332322404809702
In [ ]:
datos['Current Liabilities/Equity'].min()  # el valor más bajo de la caracteristica es cero
Out[ ]:
0.0
In [ ]:
datos['Current Liabilities/Equity'].max() # el valor más alto es 1
Out[ ]:
1.0
In [ ]:
datos['Current Liabilities/Equity'].mode()  # distribucion multimodal, varios numeros que se repiten bastante
Out[ ]:
0       0.000000
1       0.153811
2       0.234391
3       0.241053
4       0.246325
          ...   
6814    0.526052
6815    0.536635
6816    0.627817
6817    0.775890
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Current Liabilities/Equity'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.3296
Out[ ]:
0.32968513313592895
In [ ]:
datos['Current Liabilities/Equity'].mean()  # el promedio de la caracteristica es de 0.3314
Out[ ]:
0.33140980061698955
In [ ]:
datos['Current Liabilities/Equity'].std()  # los valores se alejan del promedio en 0.0134
Out[ ]:
0.013488027908897839

68.Analisis Exploratorio de la Caracteristica Long-term Liability to Current Assets

Es un índice de cobertura o solvencia que se utiliza para calcular el monto del apalancamiento de una empresa. El resultado de la relación muestra el porcentaje de los activos de una empresa que tendría que liquidar para pagar su deuda a largo plazo.

In [ ]:
datos['Long-term Liability to Current Assets'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Long-term Liability to Current Assets'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Long-term Liability to Current Assets'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor igual a  0
Out[ ]:
0.0
In [ ]:
datos['Long-term Liability to Current Assets'].quantile(0.5)   # indica que el 50 % de las empresas presentan un valor menor o igual a   0.0019
Out[ ]:
0.0019746187761809
In [ ]:
datos['Long-term Liability to Current Assets'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a   0.009
Out[ ]:
0.00900594594425662
In [ ]:
datos['Long-term Liability to Current Assets'].min()   # el valor minimo es cero
Out[ ]:
0.0
In [ ]:
datos['Long-term Liability to Current Assets'].max()  # el valor mas alto es 9540000000
Out[ ]:
9540000000.0
In [ ]:
datos['Long-term Liability to Current Assets'].mode()  # el valor que mas se repite es cero
Out[ ]:
0    0.0
dtype: float64
In [ ]:
datos['Long-term Liability to Current Assets'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.0019
Out[ ]:
0.0019746187761809
In [ ]:
datos['Long-term Liability to Current Assets'].mean()   # el promedio de la carcteristica long-term liability  54160038
Out[ ]:
54160038.13589435
In [ ]:
datos['Long-term Liability to Current Assets'].std()   # los valores se alejan del promedio en 570270621
Out[ ]:
570270621.9592273

69.Analisis Exploratorio de la Caracteristica Retained Earnings to Total Assets

Es la relación que mide la ganancia acumulada sobre el activo total de una empresa. Muestra el porcentaje del activo total que se financia con las ganancias retenidas.

In [ ]:
datos['Retained Earnings to Total Assets'].plot(kind='box')    # se observan valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Retained Earnings to Total Assets'].plot(kind='hist')    # concentracion de los valores entre 0.9 y 1
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Retained Earnings to Total Assets'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.93109
Out[ ]:
0.9310965081459854
In [ ]:
datos['Retained Earnings to Total Assets'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a  0.9376
Out[ ]:
0.937672322031461
In [ ]:
datos['Retained Earnings to Total Assets'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.9448
Out[ ]:
0.9448112860939986
In [ ]:
datos['Retained Earnings to Total Assets'].min()    # el valor mas bajo es 0
Out[ ]:
0.0
In [ ]:
datos['Retained Earnings to Total Assets'].max()   # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['Retained Earnings to Total Assets'].mode()   # distribucion multimodal , varios numeros que se repiten mucho
Out[ ]:
0       0.000000
1       0.502084
2       0.523823
3       0.592447
4       0.594171
          ...   
6814    0.995112
6815    0.995602
6816    0.996616
6817    0.998858
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Retained Earnings to Total Assets'].median()  # la mediana de Retained Earnings,  indica que el 50% de las empresas presentan un valor menor o igual a   0.93767
Out[ ]:
0.937672322031461
In [ ]:
datos['Retained Earnings to Total Assets'].mean()  # el promedio de la caracteristica es de 0.93473
Out[ ]:
0.9347327541270045
In [ ]:
datos['Retained Earnings to Total Assets'].std()  # los valores se alejan del promedio en 0.02554
Out[ ]:
0.02556422169064309

70.Analisis Exploratorio de la Caracteristica Total income/Total expense

Es la comparación de los gastos totales de una empresa con los ingresos o las ventas netas generadas.

In [ ]:
datos['Total income/Total expense'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Total income/Total expense'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Total income/Total expense'].quantile(0.25)    # indica que el 25% de las empresas presentan un valor menor o igual a  0.002235
Out[ ]:
0.002235596209657765
In [ ]:
datos['Total income/Total expense'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a  0.002336
Out[ ]:
0.00233617093104482
In [ ]:
datos['Total income/Total expense'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a  0.002491
Out[ ]:
0.0024918511193838646
In [ ]:
datos['Total income/Total expense'].min()  # el valor mas bajo es cero
Out[ ]:
0.0
In [ ]:
datos['Total income/Total expense'].max()  # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['Total income/Total expense'].mode()   # distribucion multimodal , aparecen varios numeros que se repiten varias veces
Out[ ]:
0       0.000000
1       0.000772
2       0.000971
3       0.001163
4       0.001257
          ...   
6814    0.015928
6815    0.017451
6816    0.018872
6817    0.021153
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Total income/Total expense'].median()   # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.0023361
Out[ ]:
0.00233617093104482
In [ ]:
datos['Total income/Total expense'].mean()   # el promedio de es de 0.00254
Out[ ]:
0.0025489455673864563
In [ ]:
datos['Total income/Total expense'].std()     #los valores se alejan del promedio en 0.012
Out[ ]:
0.012092814696218009

71.Analisis Exploratorio de la Caracteristica Total expense/Assets

Estos costos consisten principalmente en honorarios de administración y gastos adicionales, como honorarios de negociación, honorarios legales, honorarios de auditor y otros gastos operativos. El TER proporciona una forma de cubrir los costos anuales de funcionamiento de un fondo en particular.

In [ ]:
datos['Total expense/Assets'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Total expense/Assets'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Total expense/Assets'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.01456
Out[ ]:
0.01456705658927065
In [ ]:
datos['Total expense/Assets'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.02267
Out[ ]:
0.0226739487842648
In [ ]:
datos['Total expense/Assets'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.03593
Out[ ]:
0.035930137895265155
In [ ]:
datos['Total expense/Assets'].min()  # el vlaor mas bajo es cero
Out[ ]:
0.0
In [ ]:
datos['Total expense/Assets'].max()  # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['Total expense/Assets'].mode()  # distribucion multimodal, varios numeros que se repiten mucho
Out[ ]:
0       0.000000
1       0.000853
2       0.000895
3       0.001032
4       0.001044
          ...   
6814    0.236415
6815    0.262269
6816    0.368382
6817    0.463483
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Total expense/Assets'].median()  # la mediana de Total expense/assets indica que el 50% de las empresas presentan un valor menor o igual a  0.0226
Out[ ]:
0.0226739487842648
In [ ]:
datos['Total expense/Assets'].mean()  # el promedio es de 0.0291
Out[ ]:
0.029184099255860615
In [ ]:
datos['Total expense/Assets'].std()  # los valores se alejan del promedio en 0.02714
Out[ ]:
0.027148776792861564

72.Analisis Exploratorio de la Caracteristica Current Asset Turnover Rate

El índice de rotación de activos corrientes muestra la relación entre las ventas netas y los activos corrientes. Cuando dividimos las ventas netas con los activos corrientes y las multiplicamos por 100. En el sector minorista, un índice de rotación de activos de 2,5 o más podría considerarse bueno, mientras que una empresa del sector de servicios públicos tiene más probabilidades de apuntar a un índice de rotación de activos de entre 0,25 y 0,5.

In [ ]:
datos['Current Asset Turnover Rate'].plot(kind='box')    # se observan valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Current Asset Turnover Rate'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Current Asset Turnover Rate'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.000145
Out[ ]:
0.00014562362973872248
In [ ]:
datos['Current Asset Turnover Rate'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a  0.000198
Out[ ]:
0.000198781556663143
In [ ]:
datos['Current Asset Turnover Rate'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a  0.00045
Out[ ]:
0.000452594540757913
In [ ]:
datos['Current Asset Turnover Rate'].min()  # el valor mas bajo es cero
Out[ ]:
0.0
In [ ]:
datos['Current Asset Turnover Rate'].max()   # el valor mas alto es de 10000000000
Out[ ]:
10000000000.0
In [ ]:
datos['Current Asset Turnover Rate'].mode()  # el valor que mas se repite es 8.580000e+09
Out[ ]:
0    8.580000e+09
dtype: float64
In [ ]:
datos['Current Asset Turnover Rate'].median()  # la mediana de Current Asset Turnover rate indica que el 50% de las empresas presentan un valor menor o igual a  0.000198
Out[ ]:
0.000198781556663143
In [ ]:
datos['Current Asset Turnover Rate'].mean()   # el promedio es de 1195855763
Out[ ]:
1195855763.308841
In [ ]:
datos['Current Asset Turnover Rate'].std()   # los valores se aljena del promedio en 2821161238
Out[ ]:
2821161238.2624574

73.Analisis Exploratorio de la Caracteristica Quick Asset Turnover Rate

Generalmente, se favorece una relación más alta porque implica que la empresa es eficiente en generar ventas o ingresos a partir de su base de activos.

In [ ]:
datos['Quick Asset Turnover Rate'].plot(kind='box')   
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Quick Asset Turnover Rate'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Quick Asset Turnover Rate'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.000141
Out[ ]:
0.00014171486236357698
In [ ]:
datos['Quick Asset Turnover Rate'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.000224
Out[ ]:
0.000224772787835798
In [ ]:
datos['Quick Asset Turnover Rate'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  4900000000
Out[ ]:
4900000000.0
In [ ]:
datos['Quick Asset Turnover Rate'].min()  # el valor mas bajo es cero
Out[ ]:
0.0
In [ ]:
datos['Quick Asset Turnover Rate'].max()   # el valor mas alto es de 10000000000
Out[ ]:
10000000000.0
In [ ]:
datos['Quick Asset Turnover Rate'].mode()
Out[ ]:
0    6.460000e+09
dtype: float64
In [ ]:
datos['Quick Asset Turnover Rate'].median()  # la mediana  indica que el 50% de las empresas presentan un valor menor o igual a   0.000224
Out[ ]:
0.000224772787835798
In [ ]:
datos['Quick Asset Turnover Rate'].mean()  # el valor promedio de la caracteristica es de 2163735272
Out[ ]:
2163735272.034319
In [ ]:
datos['Quick Asset Turnover Rate'].std()   # los valores se alejan del promedio en 3374944402
Out[ ]:
3374944402.1661186

74.Analisis Exploratorio de la Caracteristica Working capitcal Turnover Rate

El índice de rotación del capital de trabajo se calcula dividiendo las ventas anuales netas de la empresa por su capital de trabajo promedio.

In [ ]:
datos['Working capitcal Turnover Rate'].plot(kind='box')   # se observan valores tipicos, valor minimo igual a cero, y la mediana cercana a 0.6
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Working capitcal Turnover Rate'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Working capitcal Turnover Rate'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.59393
Out[ ]:
0.5939344215587965
In [ ]:
datos['Working capitcal Turnover Rate'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a  0.5939
Out[ ]:
0.593962767104877
In [ ]:
datos['Working capitcal Turnover Rate'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.594
Out[ ]:
0.5940023454696104
In [ ]:
datos['Working capitcal Turnover Rate'].min()  # el valor minimo es cero
Out[ ]:
0.0
In [ ]:
datos['Working capitcal Turnover Rate'].max()   # el valor mas alto 1
Out[ ]:
1.0
In [ ]:
datos['Working capitcal Turnover Rate'].mode()   # distribucion multimodal
Out[ ]:
0       0.000000
1       0.572892
2       0.573381
3       0.588417
4       0.593136
          ...   
6814    0.656489
6815    0.665518
6816    0.674234
6817    0.706978
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Working capitcal Turnover Rate'].median()  # la mediana indica que el 50% de las empresas presentan un valor menor o igual a  0.593
Out[ ]:
0.593962767104877
In [ ]:
datos['Working capitcal Turnover Rate'].mean()  # el promedio de la caracteristica es 0.594
Out[ ]:
0.5940062655659166
In [ ]:
datos['Working capitcal Turnover Rate'].std()  # los valores se alejan en 0.0089 del promedio
Out[ ]:
0.008959384178922208

75.Analisis Exploratorio de la Caracteristica Cash Turnover Rate

El índice de rotación de efectivo es un índice de eficiencia que revela la cantidad de veces que se entrega efectivo en un período contable.

In [ ]:
datos['Cash Turnover Rate'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Cash Turnover Rate'].plot(kind='hist')    # distribucion del tipo positiva
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Cash Turnover Rate'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.00027
Out[ ]:
0.00027353373967812047
In [ ]:
datos['Cash Turnover Rate'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  1080000000
Out[ ]:
1080000000.0
In [ ]:
datos['Cash Turnover Rate'].quantile(0.75)  # indica que el % de las empresas presentan un valor menor o igual a 4510000000
Out[ ]:
4510000000.0
In [ ]:
datos['Cash Turnover Rate'].min()  # el valor minimo es cero
Out[ ]:
0.0
In [ ]:
datos['Cash Turnover Rate'].max()   # el valor maximo es de 10000000000
Out[ ]:
10000000000.0
In [ ]:
datos['Cash Turnover Rate'].mode()   # el valor que mas se repite es 1.940000e+09
Out[ ]:
0    1.940000e+09
dtype: float64
In [ ]:
datos['Cash Turnover Rate'].median()  # la mediana  indica que el 50% de las empresas presentan un valor menor o igual a  108000000
Out[ ]:
1080000000.0
In [ ]:
datos['Cash Turnover Rate'].mean()  # el promedio es de 2471976967
Out[ ]:
2471976967.444247
In [ ]:
datos['Cash Turnover Rate'].std()   # los valores se alejan del promedio en 2938623226
Out[ ]:
2938623226.6788096

76.Analisis Exploratorio de la Caracteristica Cash Flow to Sales El índice de flujo de efectivo a ventas revela la capacidad de una empresa para generar flujo de efectivo en proporción a su volumen de ventas.

In [ ]:
datos['Cash Flow to Sales'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Cash Flow to Sales'].plot(kind='hist')    # valores concentrados entre 0.65 y 0.7
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Cash Flow to Sales'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.6715652
Out[ ]:
0.6715652592532749
In [ ]:
datos['Cash Flow to Sales'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a  0.6715739
Out[ ]:
0.671573958092574
In [ ]:
datos['Cash Flow to Sales'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.671586
Out[ ]:
0.6715865804171581
In [ ]:
datos['Cash Flow to Sales'].min()  # el valor minimo es cero
Out[ ]:
0.0
In [ ]:
datos['Cash Flow to Sales'].max()  # el valor maximo es 1
Out[ ]:
1.0
In [ ]:
datos['Cash Flow to Sales'].mode()  # distribucion multimodal, varios numeros se repiten varias veces
Out[ ]:
0       0.000000
1       0.556054
2       0.652037
3       0.661814
4       0.665617
          ...   
6814    0.675956
6815    0.690841
6816    0.705789
6817    0.814676
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Cash Flow to Sales'].median()  # la mediana ndica que el 50% de las empresas presentan un valor menor o igual a   0.671573
Out[ ]:
0.671573958092574
In [ ]:
datos['Cash Flow to Sales'].mean()   # el promedio de la caracteristica es 0.671307
Out[ ]:
0.6715307810992105
In [ ]:
datos['Cash Flow to Sales'].std()   # los valores se alejan del promedio en 0.00934134
Out[ ]:
0.0093413456183006

77.Analisis Exploratorio de la Caracteristica Fixed Assets to Assets

Es una técnica de análisis financiero que muestra en términos porcentuales la parte de los activos totales de su empresa que está vinculada a los activos fijos

In [ ]:
datos['Fixed Assets to Assets'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Fixed Assets to Assets'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Fixed Assets to Assets'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.085
Out[ ]:
0.0853603651897917
In [ ]:
datos['Fixed Assets to Assets'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.1968
Out[ ]:
0.19688104822441102
In [ ]:
datos['Fixed Assets to Assets'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.37221
Out[ ]:
0.3721999782647555
In [ ]:
datos['Fixed Assets to Assets'].min()  # el valor minimo es cero
Out[ ]:
0.0
In [ ]:
datos['Fixed Assets to Assets'].max()  # el valor maximo es 8320000000
Out[ ]:
8320000000.0
In [ ]:
datos['Fixed Assets to Assets'].mode()  # el valor que mas se repite es cero
Out[ ]:
0    0.0
dtype: float64
In [ ]:
datos['Fixed Assets to Assets'].median() # # indica que el 50% de las empresas presentan un valor menor o igual a  0.1968
Out[ ]:
0.19688104822441102
In [ ]:
datos['Fixed Assets to Assets'].mean()  # el promedio es de 1220120
Out[ ]:
1220120.5015895523
In [ ]:
datos['Fixed Assets to Assets'].std()  # los valores se alejan del promedio en 100754158
Out[ ]:
100754158.71316805

78.Analisis Exploratorio de la Caracteristica Current Liability to Liability

Los pasivos corrientes son obligaciones financieras de una entidad comercial que vencen y son pagaderas dentro de un año. Este Ratio brinda una proporción del peso de los Current liability sobre el total de los compromisos

In [ ]:
datos['Current Liability to Liability'].plot(kind='box')   # se observan valores atipicos , median acercana a  0.8
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Current Liability to Liability'].plot(kind='hist')    # distribucion del tipo negativa, concentrcion de valores hacia la derecha
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Current Liability to Liability'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.6269
Out[ ]:
0.6269807662218725
In [ ]:
datos['Current Liability to Liability'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.8068
Out[ ]:
0.8068814047133329
In [ ]:
datos['Current Liability to Liability'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.9420
Out[ ]:
0.9420266937000692
In [ ]:
datos['Current Liability to Liability'].min()   # el valos minimo es cero
Out[ ]:
0.0
In [ ]:
datos['Current Liability to Liability'].max()   # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['Current Liability to Liability'].mode()   # el valor que mas se repite es 1
Out[ ]:
0    1.0
dtype: float64
In [ ]:
datos['Current Liability to Liability'].median()   # indica que el 50% de las empresas presentan un valor menor o igual a  0.8068
Out[ ]:
0.8068814047133329
In [ ]:
datos['Current Liability to Liability'].mean()   # el promedio es de 0.76159
Out[ ]:
0.761598877585336
In [ ]:
datos['Current Liability to Liability'].std()   # los valores se alejan del promedio en 0.20667
Out[ ]:
0.20667676768344168

79.Analisis Exploratorio de la Caracteristica Current Liability to Equity

La relación D / E es una métrica importante utilizada en las finanzas corporativas. Es una medida del grado en que una empresa está financiando sus operaciones a través de deuda frente a fondos de propiedad total.

In [ ]:
datos['Current Liability to Equity'].plot(kind='box')   # se observan valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Current Liability to Equity'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Current Liability to Equity'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.32809
Out[ ]:
0.328095841686878
In [ ]:
datos['Current Liability to Equity'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.3296
Out[ ]:
0.32968513313592895
In [ ]:
datos['Current Liability to Equity'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.3323
Out[ ]:
0.332322404809702
In [ ]:
datos['Current Liability to Equity'].min()  # el valor minimo es cero
Out[ ]:
0.0
In [ ]:
datos['Current Liability to Equity'].max()   # l valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['Current Liability to Equity'].mode()   # distriucion multimodal , valores que se repiten muchas veces
Out[ ]:
0       0.000000
1       0.153811
2       0.234391
3       0.241053
4       0.246325
          ...   
6814    0.526052
6815    0.536635
6816    0.627817
6817    0.775890
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Current Liability to Equity'].median()  # indica que el 50 de las empresas presentan un valor menor o igual a   0.929685
Out[ ]:
0.32968513313592895
In [ ]:
datos['Current Liability to Equity'].mean()   # el promedio es de 0.3314
Out[ ]:
0.33140980061698955
In [ ]:
datos['Current Liability to Equity'].std()  # los valores se alejan del promedio en 0.0134
Out[ ]:
0.013488027908897839

80.Analisis Exploratorio de la Caracteristica Equity to Long-term Liability

Un índice de apalancamiento que compara el monto total de la deuda a largo plazo con el capital contable de una empresa. El objetivo de esta relación es determinar cuánto apalancamiento está tomando la empresa.

In [ ]:
datos['Equity to Long-term Liability'].plot(kind='box')   # presenta valores atipicos
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Equity to Long-term Liability'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Equity to Long-term Liability'].quantile(0.25)   # indica que el 25% de las empresas presentan un valor menor o igual a  0.1109
Out[ ]:
0.11093323366346801
In [ ]:
datos['Equity to Long-term Liability'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.1123
Out[ ]:
0.11234000402497199
In [ ]:
datos['Equity to Long-term Liability'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.1171
Out[ ]:
0.11710609107562599
In [ ]:
datos['Equity to Long-term Liability'].min()  # el valor minimo es uno
Out[ ]:
0.0
In [ ]:
datos['Equity to Long-term Liability'].max()   # el valos mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['Equity to Long-term Liability'].mode()  # el valor que mas se repite es 0.1109
Out[ ]:
0    0.110933
dtype: float64
In [ ]:
datos['Equity to Long-term Liability'].median()  # indica que el 50% de las empresas presentan un valor menor o igual a  0.1123
Out[ ]:
0.11234000402497199
In [ ]:
datos['Equity to Long-term Liability'].mean()  # el promedio es 0.1156
Out[ ]:
0.11564465149636367
In [ ]:
datos['Equity to Long-term Liability'].std()  # los valores se alejan del promedio en 0.0195
Out[ ]:
0.019529176275314326

81.Analisis Exploratorio de la Caracteristica Cash Flow to Total Assets

El flujo de efectivo sobre los activos totales es un índice de eficiencia que califica realmente los flujos de efectivo a los activos de la empresa sin verse afectado por el reconocimiento de ingresos o las mediciones de ingresos.

In [ ]:
datos['Cash Flow to Total Assets'].plot(kind='box')   # se presentan valores atipicos, con una median cercana a 0.64
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Cash Flow to Total Assets'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Cash Flow to Total Assets'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.6332
Out[ ]:
0.633265319013864
In [ ]:
datos['Cash Flow to Total Assets'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.6453
Out[ ]:
0.6453664602707211
In [ ]:
datos['Cash Flow to Total Assets'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a 0.663
Out[ ]:
0.6630618534616088
In [ ]:
datos['Cash Flow to Total Assets'].min()  # el valor minimo es cero
Out[ ]:
0.0
In [ ]:
datos['Cash Flow to Total Assets'].max()  # el valos mas alto 1
Out[ ]:
1.0
In [ ]:
datos['Cash Flow to Total Assets'].mode()   # multimodal, varios valores que se repiten mucho
Out[ ]:
0       0.000000
1       0.092089
2       0.167678
3       0.284804
4       0.295268
          ...   
6814    0.928821
6815    0.935312
6816    0.963456
6817    0.970411
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Cash Flow to Total Assets'].median()  # indica que el 50% de las empresas presentan un valor menor o igual a  0.6453
Out[ ]:
0.6453664602707211
In [ ]:
datos['Cash Flow to Total Assets'].mean()  # el promedio es 0.6497
Out[ ]:
0.6497305901792345
In [ ]:
datos['Cash Flow to Total Assets'].std()  # los valores se alejan del promedio en 0.0473
Out[ ]:
0.047372131914504984

82.Analisis Exploratorio de la Caracteristica Cash Flow to Liability

La relación entre el flujo de efectivo y la deuda compara el flujo de efectivo generado por las operaciones de una empresa con su deuda total. La relación flujo de efectivo a deuda indica cuánto tiempo le tomaría a una empresa pagar toda su deuda si utilizara todo su flujo de efectivo operativo para el pago de la deuda.

In [ ]:
datos['Cash Flow to Liability'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Cash Flow to Liability'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Cash Flow to Liability'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.4571
Out[ ]:
0.4571164765642225
In [ ]:
datos['Cash Flow to Liability'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.45975
Out[ ]:
0.459750137932885
In [ ]:
datos['Cash Flow to Liability'].quantile(0.75)   # indica que el 75% de las empresas presentan un valor menor o igual a   0.464
Out[ ]:
0.4642358469715285
In [ ]:
datos['Cash Flow to Liability'].min()  # el valor mas bajo es cero
Out[ ]:
0.0
In [ ]:
datos['Cash Flow to Liability'].max()  # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['Cash Flow to Liability'].mode()  # multimodal , varios numeros que se repiten mucho
Out[ ]:
0       0.000000
1       0.028056
2       0.032583
3       0.073969
4       0.132339
          ...   
6814    0.823793
6815    0.851406
6816    0.889652
6817    0.905120
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Cash Flow to Liability'].median() # indica que el 50% de las empresas presentan un valor menor o igual a  04597
Out[ ]:
0.459750137932885
In [ ]:
datos['Cash Flow to Liability'].mean()  # el promedio es de 0.461849
Out[ ]:
0.46184925329225796
In [ ]:
datos['Cash Flow to Liability'].std()  # los valores se alejan del promedio en 0.02994
Out[ ]:
0.029942680345244797

83.Analisis Exploratorio de la Caracteristica CFO to Assets

Es un índice de eficiencia que califica los flujos de efectivo a los activos de la empresa sin verse afectado por el reconocimiento de ingresos o las mediciones de ingresos.

In [ ]:
datos['CFO to Assets'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['CFO to Assets'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['CFO to Assets'].quantile(0.25)  # indica que el  25% de las empresas presentan un valor menor o igual a  0.565
Out[ ]:
0.5659869401753584
In [ ]:
datos['CFO to Assets'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.5932
Out[ ]:
0.5932662740835439
In [ ]:
datos['CFO to Assets'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.624
Out[ ]:
0.6247688757833556
In [ ]:
datos['CFO to Assets'].min()  # el valor mas bajo es 0
Out[ ]:
0.0
In [ ]:
datos['CFO to Assets'].max()   # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['CFO to Assets'].mode()   # multimodal ,  valores que se repiten varias veces
Out[ ]:
0       0.000000
1       0.074249
2       0.178074
3       0.205248
4       0.227030
          ...   
6814    0.931068
6815    0.967086
6816    0.975197
6817    0.983205
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['CFO to Assets'].median()  # indica que el 50% de las empresas presentan un valor menor o igual a  0.593
Out[ ]:
0.5932662740835439
In [ ]:
datos['CFO to Assets'].mean()  # el promedio es 0.593
Out[ ]:
0.5934150861096208
In [ ]:
datos['CFO to Assets'].std()  # la desviacion indica en cuanto se alejan los valores del promedio en 0.0585
Out[ ]:
0.05856055014224863

84.Analisis Exploratorio de la Caracteristica Cash Flow to Equity

El flujo de efectivo a capital es una medida de cuánto efectivo está disponible para los accionistas de capital de una empresa después de que se paguen todos los gastos, reinversiones y deudas.

In [ ]:
datos['Cash Flow to Equity'].plot(kind='box')   # se observan valores atipicos  Q1 = 0.3112  Q3 0.3177, concetracion de valores entre 0.3 y 0.4
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Cash Flow to Equity'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Cash Flow to Equity'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a 0.3129
Out[ ]:
0.312994699600273
In [ ]:
datos['Cash Flow to Equity'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a 0.3149
Out[ ]:
0.314952752072916
In [ ]:
datos['Cash Flow to Equity'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.3177
Out[ ]:
0.317707188742567
In [ ]:
datos['Cash Flow to Equity'].min()   # el valor minimo 0
Out[ ]:
0.0
In [ ]:
datos['Cash Flow to Equity'].max()  # valor maximo es 1
Out[ ]:
1.0
In [ ]:
datos['Cash Flow to Equity'].mode()  # varios numeros que se repiten varias veces
Out[ ]:
0       0.000000
1       0.061964
2       0.165342
3       0.202312
4       0.240450
          ...   
6814    0.376404
6815    0.485367
6816    0.508866
6817    0.569231
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Cash Flow to Equity'].median()  # indica que el  50 % de las empresas presentan un valor menor o igual a 0.3149
Out[ ]:
0.314952752072916
In [ ]:
datos['Cash Flow to Equity'].mean()  # el prdio de cash flow to equity es de 0.31558
Out[ ]:
0.31558238989957665
In [ ]:
datos['Cash Flow to Equity'].std()   # los valores se alejan del promedio en 0.012
Out[ ]:
0.012960892401647255

85.Analisis Exploratorio de la Caracteristica Current Liability to Current Assets

Realiza una comparación de los compromisos circulantes con los Activos circulantes.

In [ ]:
datos['Current Liability to Current Assets'].plot(kind='box')   # se observan valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Current Liability to Current Assets'].plot(kind='hist')    # dsitribucion del tipo positiva
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Current Liability to Current Assets'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.018
Out[ ]:
0.018033665707965
In [ ]:
datos['Current Liability to Current Assets'].quantile(0.5)   # indica que el 50% de las empresas presentan un valor menor o igual a  0.02759
Out[ ]:
0.0275971428517009
In [ ]:
datos['Current Liability to Current Assets'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.0383
Out[ ]:
0.0383746158541899
In [ ]:
datos['Current Liability to Current Assets'].min()  # el valor mas bajo es 0
Out[ ]:
0.0
In [ ]:
datos['Current Liability to Current Assets'].max()  # el valor mas alto es de 1
Out[ ]:
1.0
In [ ]:
datos['Current Liability to Current Assets'].mode()  # multimodal varios numeros que se repiten mucho
Out[ ]:
0       0.000000
1       0.000122
2       0.000214
3       0.000220
4       0.000279
          ...   
6814    0.460675
6815    0.611724
6816    0.650661
6817    0.916814
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Current Liability to Current Assets'].median() # indica que el 50% de las empresas presentan un valor menor o igual a  0.0275
Out[ ]:
0.0275971428517009
In [ ]:
datos['Current Liability to Current Assets'].mean()  # el promedio es de 0.0315
Out[ ]:
0.031506365747440715
In [ ]:
datos['Current Liability to Current Assets'].std()  # los valores se alejan del promedio en 0.03084
Out[ ]:
0.030844688453563838

86.Analisis Exploratorio de la Caracteristica Liability-Assets Flag

indica el estado de una organización, donde si el pasivo total excede los activos totales, el valor marcado será 1, de lo contrario el valor es 0. La mayoría de las veces, los activos de las organizaciones / empresas son más que sus pasivo.

Una bandera roja es una advertencia o indicador que sugiere que existe un problema o amenaza potencial con las acciones, los estados financieros o los informes de noticias de una empresa. Las señales de alerta pueden ser cualquier característica indeseable que se destaque para un analista o inversor.

In [ ]:
datos['Liability-Assets Flag'].plot(kind='box')    # caracteristica binaria , la moda es 0
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Liability-Assets Flag'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Liability-Assets Flag'].quantile(0.25)
Out[ ]:
0.0
In [ ]:
datos['Liability-Assets Flag'].quantile(0.5)
Out[ ]:
0.0
In [ ]:
datos['Liability-Assets Flag'].quantile(0.75)
Out[ ]:
0.0
In [ ]:
datos['Liability-Assets Flag'].mode()  # el valor que mas se repite es 0
Out[ ]:
0    0
dtype: int64

87.Analisis Exploratorio de la Caracteristica Net Income to Total Assets

El rendimiento de los activos (ROA) es un índice financiero que muestra el porcentaje de ganancias que obtiene una empresa en relación con sus recursos generales. Se define comúnmente como el ingreso neto dividido por los activos totales.

In [ ]:
datos['Net Income to Total Assets'].plot(kind='box')  # se observan valores atipicos , con una mediana cercana a 0.8
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Net Income to Total Assets'].plot(kind='hist')    # distribucion tipo negativa
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Net Income to Total Assets'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.796
Out[ ]:
0.7967498491931704
In [ ]:
datos['Net Income to Total Assets'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.8106
Out[ ]:
0.8106190420751009
In [ ]:
datos['Net Income to Total Assets'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.8264
Out[ ]:
0.8264545295408714
In [ ]:
datos['Net Income to Total Assets'].min()  # el valor mas bajo es 0
Out[ ]:
0.0
In [ ]:
datos['Net Income to Total Assets'].max()  # el valor es mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['Net Income to Total Assets'].mode()   # multimodal, varios valores que se repitan mucho
Out[ ]:
0       0.000000
1       0.224792
2       0.411809
3       0.412621
4       0.420995
          ...   
6814    0.944328
6815    0.959320
6816    0.981315
6817    0.982879
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Net Income to Total Assets'].median() # indica que el 50% de las empresas presentan un valor menor o igual a  0.8106
Out[ ]:
0.8106190420751009
In [ ]:
datos['Net Income to Total Assets'].mean()  # el promedio es de 0.8077
Out[ ]:
0.807760220036551
In [ ]:
datos['Net Income to Total Assets'].std()  # los valores se alejan del promedio en 0.0403
Out[ ]:
0.0403321915314262

88.Analisis Exploratorio de la Caracteristica Total assets to GNP price

Esta caracteristica parece ser un proceso reexpresión por efecto de moneda. (Índice de nivel de precios de activos totales / PNB), una medida de activos reales (RASSET) y Talla. Dado que los activos totales se informan en dólares

In [ ]:
datos['Total assets to GNP price'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})                    # se observa valores atipicos 
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Total assets to GNP price'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Total assets to GNP price'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.000903
Out[ ]:
0.000903620481330612
In [ ]:
datos['Total assets to GNP price'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.002085
Out[ ]:
0.00208521270881575
In [ ]:
datos['Total assets to GNP price'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.00826
Out[ ]:
0.0052697768568805495
In [ ]:
datos['Total assets to GNP price'].min()  # valor mas bajo es 0
Out[ ]:
0.0
In [ ]:
datos['Total assets to GNP price'].max()  # el valor mas alto es 9820000000
Out[ ]:
9820000000.0
In [ ]:
datos['Total assets to GNP price'].mode()  # el valos que mas se repite es 0.003661
Out[ ]:
0    0.003661
dtype: float64
In [ ]:
datos['Total assets to GNP price'].median()   # indica que el 50 % de las empresas presentan un valor menor o igual a  0.00208
Out[ ]:
0.00208521270881575
In [ ]:
datos['Total assets to GNP price'].mean()   # el promedio es de 18629417
Out[ ]:
18629417.811835933
In [ ]:
datos['Total assets to GNP price'].std()  # los valores se pueden alejar del promedio en 376450059
Out[ ]:
376450059.74582857

89.Analisis Exploratorio de la Caracteristica No-credit Interval

Indica durante cuánto tiempo la empresa podría satisfacer las necesidades operativas de su stock de activos defensivos después de liquidar todos los pasivos corrientes existentes y pagar en efectivo todas las adquisiciones futuras.

In [ ]:
datos['No-credit Interval'].plot(kind='box')     # se observan valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['No-credit Interval'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['No-credit Interval'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.6236
Out[ ]:
0.6236363049739091
In [ ]:
datos['No-credit Interval'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.6238
Out[ ]:
0.6238792259877121
In [ ]:
datos['No-credit Interval'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a 0.624
Out[ ]:
0.6241681927893561
In [ ]:
datos['No-credit Interval'].min()   # el valor minimo es cero
Out[ ]:
0.0
In [ ]:
datos['No-credit Interval'].max()  # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['No-credit Interval'].mode()  # distribucion multimodal
Out[ ]:
0       0.000000
1       0.408682
2       0.419045
3       0.528279
4       0.531839
          ...   
6814    0.792048
6815    0.797385
6816    0.841360
6817    0.956387
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['No-credit Interval'].median() # indica que el 50% de las empresas presentan un valor menor o igual a  0.6238
Out[ ]:
0.6238792259877121
In [ ]:
datos['No-credit Interval'].mean()   # el promedio es 0.623914
Out[ ]:
0.623914574767535
In [ ]:
datos['No-credit Interval'].std()  # los valores se desbian del promedio en 0.6239
Out[ ]:
0.012289548007412275

90.Analisis Exploratorio de la Caracteristica Gross Profit to Sales

La ganancia bruta sirve como métrica financiera utilizada para determinar la rentabilidad bruta de una operación comercial.

In [ ]:
datos['Gross Profit to Sales'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Gross Profit to Sales'].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Gross Profit to Sales'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.6004
Out[ ]:
0.6004428952063054
In [ ]:
datos['Gross Profit to Sales'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.6059
Out[ ]:
0.605998288167218
In [ ]:
datos['Gross Profit to Sales'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.6139
Out[ ]:
0.613913271038147
In [ ]:
datos['Gross Profit to Sales'].min()  # valos minimo es 0
Out[ ]:
0.0
In [ ]:
datos['Gross Profit to Sales'].max()   # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['Gross Profit to Sales'].mode()  # el valor que mas se repite es 0.665149
Out[ ]:
0    0.665149
dtype: float64
In [ ]:
datos['Gross Profit to Sales'].median()  # indica que el 50% de las empresas presentan un valor menor o igual a  .6059
Out[ ]:
0.605998288167218
In [ ]:
datos['Gross Profit to Sales'].mean()   # el promedio es 0.6079
Out[ ]:
0.6079463402707161
In [ ]:
datos['Gross Profit to Sales'].std()  # los valores se desvian del promedio en 0.0169
Out[ ]:
0.01693380779567362

91.Analisis Exploratorio de la Caracteristica Net Income to Stockholder's Equity

El rendimiento sobre el capital (ROE) es una medida del desempeño financiero que se calcula dividiendo la utilidad neta por el capital contable. Dado que el capital contable es igual a los activos de una empresa menos su deuda, el ROE se considera el rendimiento de los activos netos.

In [ ]:
datos["Net Income to Stockholder's Equity"].plot(kind='box')   # se observan valores atipicos , mediana cercana a 0.84
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos["Net Income to Stockholder's Equity"].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos["Net Income to Stockholder's Equity"].quantile(0.25) # indica que el 25% de las empresas presentan un valor menor o igual a  0.84101
Out[ ]:
0.8401148040637194
In [ ]:
datos["Net Income to Stockholder's Equity"].quantile(0.5) # indica que el 50% de las empresas presentan un valor menor o igual a  0.84117
Out[ ]:
0.841178760250192
In [ ]:
datos["Net Income to Stockholder's Equity"].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.8423
Out[ ]:
0.8423569700412374
In [ ]:
datos["Net Income to Stockholder's Equity"].min()  # el valor mas bajo es cero
Out[ ]:
0.0
In [ ]:
datos["Net Income to Stockholder's Equity"].max()   # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos["Net Income to Stockholder's Equity"].mode()  # distribucion multimodal
Out[ ]:
0       0.000000
1       0.344652
2       0.442176
3       0.634587
4       0.637576
          ...   
6814    0.902744
6815    0.916329
6816    0.976180
6817    0.996912
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos["Net Income to Stockholder's Equity"].median() # indica que el 50% de las empresas presentan un valor menor o igual a  0.8411
Out[ ]:
0.841178760250192
In [ ]:
datos["Net Income to Stockholder's Equity"].mean()  # el promedio es 0.8404
Out[ ]:
0.8404020646301001
In [ ]:
datos["Net Income to Stockholder's Equity"].std()  # los valores se desvian e 0.01452
Out[ ]:
0.014522526082524962

92.Analisis Exploratorio de la Caracteristica Liability to Equity

compara los pasivos totales de una empresa con el capital contable y se puede utilizar para evaluar cuánto apalancamiento está utilizando una empresa

In [ ]:
datos["Liability to Equity"].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos["Liability to Equity"].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos["Liability to Equity"].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.2769
Out[ ]:
0.276944242646329
In [ ]:
datos["Liability to Equity"].quantile(0.5) # indica que el 50% de las empresas presentan un valor menor o igual a  0.2787
Out[ ]:
0.27877758362963695
In [ ]:
datos["Liability to Equity"].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.2814
Out[ ]:
0.28144918560882654
In [ ]:
datos["Liability to Equity"].min()   # el valor minimo es cero
Out[ ]:
0.0
In [ ]:
datos["Liability to Equity"].max()  # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos["Liability to Equity"].mode()   # multimodal, varios numeros que se repiten varias veces
Out[ ]:
0       0.000000
1       0.133503
2       0.182790
3       0.199162
4       0.209222
          ...   
6814    0.484318
6815    0.643692
6816    0.652347
6817    0.745352
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos["Liability to Equity"].median()  # indica que el 50% de las empresas presentan un valor menor o igual a  0.2787
Out[ ]:
0.27877758362963695
In [ ]:
datos["Liability to Equity"].mean()  # el promedio de la caracteristica es 0.28036
Out[ ]:
0.28036515383339244
In [ ]:
datos["Liability to Equity"].std()   # los valores se alejan del promedio en 0.014
Out[ ]:
0.01446322357559402

93.Analisis Exploratorio de la Caracteristica Degree of Financial Leverage (DFL)

El grado de apalancamiento financiero (DFL) es un índice de apalancamiento que mide la sensibilidad de las ganancias por acción de una empresa a las fluctuaciones en sus ingresos operativos, como resultado de cambios en su estructura de capital.

In [ ]:
datos["Degree of Financial Leverage (DFL)"].plot(kind='box')   # valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos["Degree of Financial Leverage (DFL)"].plot(kind='hist')    # distribucion del tipo positiva , valores concentrados hacia la izquierda
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos["Degree of Financial Leverage (DFL)"].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.02679
Out[ ]:
0.0267911566924924
In [ ]:
datos["Degree of Financial Leverage (DFL)"].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.02680
Out[ ]:
0.0268081258982465
In [ ]:
datos["Degree of Financial Leverage (DFL)"].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.02691
Out[ ]:
0.026913184214613348
In [ ]:
datos["Degree of Financial Leverage (DFL)"].min()  # el valor minimo es 0
Out[ ]:
0.0
In [ ]:
datos["Degree of Financial Leverage (DFL)"].max()  # el valor maximo es 1
Out[ ]:
1.0
In [ ]:
datos["Degree of Financial Leverage (DFL)"].mode()   # el valor que mas se repite es 0.026
Out[ ]:
0    0.026791
dtype: float64
In [ ]:
datos["Degree of Financial Leverage (DFL)"].median() # indica que el 50 % de las empresas presentan un valor menor o igual a  0.026
Out[ ]:
0.0268081258982465
In [ ]:
datos["Degree of Financial Leverage (DFL)"].mean()  # el promedio es de 0.0275
Out[ ]:
0.02754111942120396
In [ ]:
datos["Degree of Financial Leverage (DFL)"].std()  # los valores se alejan del promedio en 0.0156
Out[ ]:
0.01566794186642957

94.Analisis Exploratorio de la Caracteristica Interest Coverage Ratio (Interest expense to EBIT)

El índice de cobertura de intereses se calcula dividiendo las ganancias de una empresa antes de intereses e impuestos (EBIT) por su gasto por intereses durante un período determinado. El índice de cobertura de intereses a veces se denomina índice multiplicado por intereses devengados (TIE).

In [ ]:
datos["Interest Coverage Ratio (Interest expense to EBIT)"].plot(kind='box')   # valores atipicios 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos["Interest Coverage Ratio (Interest expense to EBIT)"].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos["Interest Coverage Ratio (Interest expense to EBIT)"].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a  0.56515
Out[ ]:
0.5651583957576041
In [ ]:
datos["Interest Coverage Ratio (Interest expense to EBIT)"].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.56525
Out[ ]:
0.565251928758969
In [ ]:
datos["Interest Coverage Ratio (Interest expense to EBIT)"].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.05657
Out[ ]:
0.565724709506105
In [ ]:
datos["Interest Coverage Ratio (Interest expense to EBIT)"].min()   # el valor minimo es 0
Out[ ]:
0.0
In [ ]:
datos["Interest Coverage Ratio (Interest expense to EBIT)"].max()  # el valor maximo es 1
Out[ ]:
1.0
In [ ]:
datos["Interest Coverage Ratio (Interest expense to EBIT)"].mode()  # el valor que mas se repite es 0.565158
Out[ ]:
0    0.565158
dtype: float64
In [ ]:
datos["Interest Coverage Ratio (Interest expense to EBIT)"].median()  # indica que el 50% de las empresas presentan un valor menor o igual a  0.56525192
Out[ ]:
0.565251928758969
In [ ]:
datos["Interest Coverage Ratio (Interest expense to EBIT)"].mean()  # el promedio la caracteristica es 0.56535793
Out[ ]:
0.5653579335465493
In [ ]:
datos["Interest Coverage Ratio (Interest expense to EBIT)"].std()  # los valores se alejan del promedio en 0.0132142
Out[ ]:
0.013214239761962017

95.Analisis Exploratorio de la Caracteristica Net Income Flag

La bandera de "Ingresos netos" denota el estado de los ingresos de una organización en los últimos dos años, donde si los ingresos netos son negativos durante los últimos dos años, el valor marcado será 1, de lo contrario el valor es 0. Observamos que todos los Los registros han estado mostrando pérdidas durante los últimos dos años.

In [ ]:
datos["Net Income Flag"].plot(kind='box')   # caracteristica binaria
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos['Net Income Flag'].quantile(0.25)
Out[ ]:
1.0
In [ ]:
datos['Net Income Flag'].quantile(0.5)
Out[ ]:
1.0
In [ ]:
datos['Net Income Flag'].quantile(0.75)
Out[ ]:
1.0
In [ ]:
datos['Net Income Flag'].mode()   # el valor que mas se repite es 1
Out[ ]:
0    1
dtype: int64

96.Analisis Exploratorio de la Caracteristica Equity to Liability

La relación deuda-capital (D / E) se utiliza para evaluar el apalancamiento financiero de una empresa y se calcula dividiendo los pasivos totales de una empresa por su capital social.

In [ ]:
datos["Equity to Liability"].plot(kind='box')   # valores atipicos 
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Histograma y Medidas de tendencia central y otros valores de relevancia en el análisis

In [ ]:
datos["Equity to Liability"].plot(kind='hist')  
sns.set(rc={'figure.figsize':(7,7)})  
plt.show()
In [ ]:
datos['Equity to Liability'].quantile(0.25)  # indica que el 25% de las empresas presentan un valor menor o igual a   0.0244
Out[ ]:
0.024476693570910098
In [ ]:
datos['Equity to Liability'].quantile(0.5)  # indica que el 50% de las empresas presentan un valor menor o igual a  0.03379
Out[ ]:
0.0337976972031022
In [ ]:
datos['Equity to Liability'].quantile(0.75)  # indica que el 75% de las empresas presentan un valor menor o igual a  0.05283
Out[ ]:
0.052837817459331596
In [ ]:
datos['Equity to Liability'].min()   # el valor minimo es cero
Out[ ]:
0.0
In [ ]:
datos['Equity to Liability'].max()  # el valor mas alto es 1
Out[ ]:
1.0
In [ ]:
datos['Equity to Liability'].mode()  # distribucion de los valores multimodal, varios valores se repiten mucho
Out[ ]:
0       0.000000
1       0.003946
2       0.008500
3       0.008753
4       0.008950
          ...   
6814    0.798122
6815    0.881018
6816    0.920638
6817    0.942729
6818    1.000000
Length: 6819, dtype: float64
In [ ]:
datos['Equity to Liability'].median()  # indica que el 50 % de las empresas presentan un valor menor o igual a  0.033797
Out[ ]:
0.0337976972031022
In [ ]:
datos['Equity to Liability'].mean()   # el promedio es de 0.0475
Out[ ]:
0.04757835652949777
In [ ]:
datos['Equity to Liability'].std()   # los valores se alejan del promedio en 0.05001
Out[ ]:
0.05001371618013793

Tablas Pivote y Gráficas

Recordemos que Bankrupt?: Aparece como una caracteristica binaria, en donde si indica cero 0, nos dice que la empresa es competente, mientras que si el valor es 1, nos indica que es probable que la empresa caiga en banca rota.

Tabla #1 Ver Según la Categoria de Bankrupt

In [ ]:
print(datos["Bankrupt?"].value_counts())         # se representa como se encuentra distribuida las empresas , tenemos 6599 empresas como competente
                                                  # mientras que empresas no competente corresponden a 220
0    6599
1     220
Name: Bankrupt?, dtype: int64
In [ ]:
labelsbank= ['Competente', 'No Competente']
plt.pie(datos["Bankrupt?"].value_counts(),labels = labelsbank)
plt.show()
In [ ]:
print(datos[['Net Income Flag','Bankrupt?']].value_counts())     # distribucion de las emrpesas segun la caracteristica de Net Income Flag, la mayoria de las 
sns.countplot(x = 'Net Income Flag',hue = 'Bankrupt?', data=datos)   # empresas de tipo que no tienen riesgo de bancarrota segun net income son 6599
Net Income Flag  Bankrupt?
1                0            6599
                 1             220
dtype: int64
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7efc7ed2b510>
In [ ]:
datos.groupby(['Bankrupt?']).mean()    # se procede a presentar los valores promedio de las caracteristicas segun si la empresa tiene o no riesfo de caer en bancarrota
Out[ ]:
ROA(C) before interest and depreciation before interest ROA(A) before interest and % after tax ROA(B) before interest and depreciation after tax Operating Gross Margin Realized Sales Gross Margin Operating Profit Rate Pre-tax net Interest Rate After-tax net Interest Rate Non-industry income and expenditure/revenue Continuous interest rate (after tax) Operating Expense Rate Research and development expense rate Cash flow rate Interest-bearing debt interest rate Tax rate (A) Net Value Per Share (B) Net Value Per Share (A) Net Value Per Share (C) Persistent EPS in the Last Four Seasons Cash Flow Per Share Revenue Per Share (Yuan ¥) Operating Profit Per Share (Yuan ¥) Per Share Net profit before tax (Yuan ¥) Realized Sales Gross Profit Growth Rate Operating Profit Growth Rate After-tax Net Profit Growth Rate Regular Net Profit Growth Rate Continuous Net Profit Growth Rate Total Asset Growth Rate Net Value Growth Rate Total Asset Return Growth Rate Ratio Cash Reinvestment % Current Ratio Quick Ratio Interest Expense Ratio Total debt/Total net worth Debt ratio % Net worth/Assets Long-term fund suitability ratio (A) Borrowing dependency ... Current Assets/Total Assets Cash/Total Assets Quick Assets/Current Liability Cash/Current Liability Current Liability to Assets Operating Funds to Liability Inventory/Working Capital Inventory/Current Liability Current Liabilities/Liability Working Capital/Equity Current Liabilities/Equity Long-term Liability to Current Assets Retained Earnings to Total Assets Total income/Total expense Total expense/Assets Current Asset Turnover Rate Quick Asset Turnover Rate Working capitcal Turnover Rate Cash Turnover Rate Cash Flow to Sales Fixed Assets to Assets Current Liability to Liability Current Liability to Equity Equity to Long-term Liability Cash Flow to Total Assets Cash Flow to Liability CFO to Assets Cash Flow to Equity Current Liability to Current Assets Liability-Assets Flag Net Income to Total Assets Total assets to GNP price No-credit Interval Gross Profit to Sales Net Income to Stockholder's Equity Liability to Equity Degree of Financial Leverage (DFL) Interest Coverage Ratio (Interest expense to EBIT) Net Income Flag Equity to Liability
Bankrupt?
0 0.508069 0.562015 0.556659 0.608257 0.608237 0.998756 0.79721 0.809106 0.303657 0.781401 1.998943e+09 1.961923e+09 0.467656 1.690392e+07 0.117778 0.191669 0.191644 0.191680 0.230146 0.323731 1.372935e+06 0.109815 0.185581 0.022409 0.848010 0.689242 0.689244 0.217656 5.531603e+09 2.045765e+05 0.264277 0.379871 416729.822913 7.257160e+06 0.630997 4.037733e+06 0.110714 0.889286 0.008696 0.374129 ... 0.524058 0.126640 3.712684e+06 2.989953e+07 0.088887 0.354323 0.277399 5.571944e+07 0.762384 0.736130 0.331031 5.407900e+07 0.935749 0.002565 0.028495 1.189712e+09 2.147829e+09 0.594011 2.481653e+09 0.671530 2.468865e-01 0.762384 0.331031 0.115149 0.650340 0.462085 0.594649 0.315721 0.030542 0.000303 0.810083 1.621670e+07 0.623927 0.608256 0.840882 0.279925 0.027511 0.565371 1.0 0.048337
1 0.418503 0.456947 0.461483 0.598670 0.598717 0.998739 0.79659 0.808424 0.302609 0.780799 1.887486e+09 1.605623e+09 0.460681 2.772727e+06 0.031690 0.160416 0.160301 0.160459 0.188818 0.316006 2.732298e-02 0.087354 0.147765 0.022378 0.847087 0.686280 0.686345 0.217121 4.803017e+09 4.240909e+07 0.263358 0.373846 0.007238 4.195455e+07 0.630826 1.577273e+07 0.187047 0.812953 0.011391 0.390400 ... 0.468733 0.047736 5.020996e-03 2.549409e+08 0.144238 0.338991 0.277286 5.842727e+07 0.738046 0.726401 0.342772 5.659091e+07 0.904244 0.002076 0.049858 1.380154e+09 2.640836e+09 0.593864 2.181733e+09 0.671555 3.781818e+07 0.738046 0.342772 0.130512 0.631452 0.454778 0.556412 0.311426 0.060443 0.027273 0.738083 9.100000e+07 0.623541 0.598669 0.826008 0.293578 0.028443 0.564959 1.0 0.024832

2 rows × 95 columns

In [ ]:
datos.pivot_table('Accounts Receivable Turnover', index= 'Operating Gross Margin', columns= 'Bankrupt?', aggfunc='mean',fill_value=0)
Out[ ]:
Bankrupt? 0 1
Operating Gross Margin
0.000000 4.935631e-02 0.000000e+00
0.156308 1.793595e-03 0.000000e+00
0.432653 2.982135e-01 0.000000e+00
0.445646 1.637365e-03 0.000000e+00
0.448342 8.562182e-04 0.000000e+00
... ... ...
0.664553 7.446258e-04 0.000000e+00
0.664560 7.182494e-04 0.000000e+00
0.664870 7.588285e-04 0.000000e+00
0.665151 1.718050e-02 1.220000e+09
1.000000 4.870000e+09 0.000000e+00

3781 rows × 2 columns

In [ ]:
plt.figure(figsize = (20,20))
datos.pivot_table('Accounts Receivable Turnover', index= 'Operating Gross Margin', columns= 'Bankrupt?', aggfunc='mean',fill_value=0).plot()
plt.title("Operating Gross Margin por Bankrupt?")
plt.ylabel("Bankrupt?")
plt.xlabel("Operating Gross Margin")
plt.show();

# se busca ver como se comporta os valores del gross margin y accounts recivable , segun si la empresa tiene o no riesgo de bancarrota, parece que las empresas
# que son mas competentes o no tienen riesgo de caer en bancarrota tienen un valor en estas caracteristicas mayor que aquellas que si tienen riesgo de bancarrota
<Figure size 1440x1440 with 0 Axes>
In [ ]:
datos.pivot_table('Operating Profit Growth Rate', index= 'Operating Gross Margin', columns= 'Bankrupt?', aggfunc='mean',fill_value=0)
Out[ ]:
Bankrupt? 0 1
Operating Gross Margin
0.000000 0.847191 0.00000
0.156308 0.847906 0.00000
0.432653 0.831638 0.00000
0.445646 0.847889 0.00000
0.448342 0.846758 0.00000
... ... ...
0.664553 0.848025 0.00000
0.664560 0.848005 0.00000
0.664870 0.847966 0.00000
0.665151 0.848059 0.84778
1.000000 0.848075 0.00000

3781 rows × 2 columns

In [ ]:
plt.figure(figsize = (20,20))
datos.pivot_table('Operating Profit Growth Rate', index= 'Operating Gross Margin', columns= 'Bankrupt?', aggfunc='mean',fill_value=0).plot()
plt.title("Operating Profit Growth Rate por Bankrupt?")
plt.ylabel("Bankrupt?")
plt.xlabel("Operating Gross Margin")
plt.show();

#  segun si la empresa tiene o no riesgo de bancarrota, parece que las empresas
# que son mas competentes o no tienen riesgo de caer en bancarrota tienen un valor en estas caracteristicas mayor que aquellas que si tienen riesgo de bancarrota
<Figure size 1440x1440 with 0 Axes>

Verificar si a patir de la ROA(A) before interest and % after tax se oberva comportamiento de las empresas

Primero se toman o eligen caracteristicas de interes, luego se ordan según orden descendente y /o ascendente para ver las empresas que (datos) que aparecen .Según la matriz de correlación ROA(A) before interest and % after tax y Bankrupt tienen una correlación de 0.282941.

Ver resultados de Tabla 1 ROA(A) before interest and % after tax descendente junto con tabla 2 ROA(A) before interest and % after tax ascendente

In [ ]:
prueba1= datos.iloc[:,[0,1,2,6,21,22,25,29,30,35,36]]
In [ ]:
table1 = prueba1.sort_values('ROA(A) before interest and % after tax',ascending=False)
In [ ]:
table1.head(30)   # Las empresas que tienne el valor de ROA(A) before interest and % after tax más alto pertenecen a la categoría Bankrupt 0 (competente)
Out[ ]:
Bankrupt? ROA(C) before interest and depreciation before interest ROA(A) before interest and % after tax Operating Profit Rate Revenue Per Share (Yuan ¥) Operating Profit Per Share (Yuan ¥) Operating Profit Growth Rate Total Asset Growth Rate Net Value Growth Rate Interest Expense Ratio Total debt/Total net worth
4877 0 0.971530 1.000000 0.999571 0.046887 0.228483 0.863892 2.114707e-04 0.001344 0.630641 0.003080
3682 0 0.572710 0.984736 0.999458 0.015654 0.131423 0.848104 1.837966e-04 0.000826 0.630614 0.000866
3532 0 0.687954 0.954536 0.999327 0.018951 0.127514 0.848787 1.092077e-04 0.000639 0.630616 0.001018
6610 0 1.000000 0.947067 0.999507 0.355158 1.000000 0.848584 3.612842e-04 0.001169 0.630612 0.007519
3385 0 0.864964 0.942706 0.999308 0.057141 0.187770 0.848574 2.135549e-04 0.000861 0.630612 0.001930
4734 0 0.780139 0.875600 0.999592 0.095376 0.375865 0.848357 2.024176e-04 0.000951 0.630637 0.004972
5393 0 0.759811 0.831716 0.999413 0.047522 0.195261 0.848253 1.309752e-04 0.000779 0.630623 0.001749
4481 0 0.730074 0.820159 0.999165 0.049080 0.142008 0.848051 1.359081e-04 0.000727 0.630637 0.003039
4530 0 0.775264 0.816016 0.999441 0.046387 0.199088 0.848175 1.451864e-04 0.000682 0.630612 0.002046
4114 0 0.754643 0.809256 0.999376 0.062238 0.215618 0.848786 1.583354e-04 0.000767 0.630682 0.004760
3207 0 0.721737 0.806749 0.999459 0.097751 0.322042 0.848172 1.200954e-04 0.000647 0.630612 0.002373
4636 0 0.731829 0.805877 0.999554 0.043136 0.214478 0.858322 2.362236e-04 0.000783 0.630652 0.006577
4621 0 0.787988 0.804514 0.999147 0.078059 0.162935 0.848152 2.036810e-04 0.001017 0.630613 0.003754
6565 0 0.818018 0.801570 0.999278 0.108308 0.255354 0.848179 1.171260e-04 0.000830 0.630623 0.004686
3365 0 0.701945 0.799989 0.999296 0.042667 0.161795 0.848118 1.418307e-04 0.000664 0.630613 0.001693
4824 0 0.721347 0.797209 0.999521 0.045238 0.213256 0.848067 8.180000e+09 0.000489 0.630612 0.001261
5163 0 0.715351 0.790558 0.999253 0.363568 0.590994 0.848163 1.165064e-04 0.000658 0.630612 0.004882
4618 0 0.719641 0.788432 0.999499 0.032624 0.177021 0.848094 1.092157e-04 0.000630 0.630612 0.000881
3374 0 0.695364 0.786797 0.999185 0.066609 0.164889 0.848840 1.443334e-04 0.000890 0.630612 0.003012
5609 0 0.734997 0.782545 0.999133 0.028117 0.117743 0.848931 1.200069e-04 0.000749 0.630616 0.003938
4217 0 0.755131 0.779546 0.999188 0.260478 0.371468 0.850837 1.171260e-04 0.000769 0.630613 0.005160
3433 0 0.691756 0.777747 0.999281 0.071994 0.202834 0.848276 1.142291e-04 0.000651 0.630617 0.003101
2290 0 0.685760 0.771696 0.999312 0.040307 0.161225 0.848099 1.264929e-04 0.000626 0.630655 0.002235
6018 0 0.709648 0.769298 0.999110 0.086634 0.155525 0.850254 1.271463e-03 0.003482 0.630636 0.027216
4766 0 0.694730 0.768371 0.999283 0.077605 0.211953 0.848195 1.076143e-04 0.000662 0.630622 0.002518
3517 0 0.683030 0.767390 0.999274 0.063221 0.187688 0.848264 1.166271e-04 0.000646 0.630612 0.002950
3571 0 0.686638 0.765973 0.999315 0.064477 0.201775 0.848193 1.069867e-04 0.000591 0.630614 0.002371
2123 0 0.691513 0.764719 0.999191 0.051923 0.150965 0.848752 3.588056e-04 0.001441 0.630614 0.005011
2242 0 0.711890 0.763519 0.999301 0.044346 0.165459 0.848306 1.244328e-04 0.000617 0.630612 0.001907
3726 0 0.683274 0.761666 0.999315 0.077181 0.222702 0.848086 9.590000e+09 0.000554 0.630612 0.002059

Tabla 2 ROA(A) before interest and % after tax Ascendente

In [ ]:
table2 = prueba1.sort_values('ROA(A) before interest and % after tax',ascending=True)
In [ ]:
table2.head(30)   # Una vez que se coloca el orden para observar los valores más bajos en la caracteristica OA(A) before interest and % after tax
                 # aparecen empresas en categoría 1 (que podrían caer en Bankrupt)
Out[ ]:
Bankrupt? ROA(C) before interest and depreciation before interest ROA(A) before interest and % after tax Operating Profit Rate Revenue Per Share (Yuan ¥) Operating Profit Per Share (Yuan ¥) Operating Profit Growth Rate Total Asset Growth Rate Net Value Growth Rate Interest Expense Ratio Total debt/Total net worth
1443 1 0.024277 0.000000 0.994015 0.001906 0.051136 0.846272 2.097566e-04 7.359047e-04 0.630612 0.002850
1035 0 0.000000 0.006923 0.998775 0.005626 0.090302 0.844483 1.320000e+09 2.339473e-04 0.630608 0.014798
56 1 0.066933 0.057185 0.998825 0.045843 0.066200 0.847569 7.230000e+09 1.542430e-04 0.630523 0.126572
1684 0 0.082826 0.069287 0.997083 0.003796 0.061966 0.847816 0.000000e+00 2.298299e-04 0.630612 0.013659
2001 1 0.438795 0.090166 0.997789 0.003721 0.074912 0.736430 2.490000e+08 1.670197e-04 0.630547 0.090127
2435 0 0.264320 0.090711 0.998241 0.005611 0.076378 0.847774 3.260000e+09 3.524806e-04 0.630598 0.001093
3392 0 0.081412 0.091256 0.973875 0.000393 0.048530 0.847620 2.440000e+09 2.835317e-04 0.630612 0.004692
4918 1 0.126456 0.106629 0.998721 0.031066 0.060500 0.847457 2.410000e+09 2.269851e-04 0.630569 0.041594
3597 1 0.242822 0.119930 0.998155 0.009302 0.060256 0.847877 2.210000e+09 2.974562e-04 0.630612 0.002866
3595 1 0.102325 0.121511 0.998786 0.038553 0.063838 0.847393 3.450000e+09 2.920661e-04 0.630546 0.016879
5228 1 0.388193 0.132959 0.998647 0.009392 0.081508 0.848082 2.230000e+09 2.622206e-04 0.630592 0.012950
1951 1 0.208161 0.160870 0.998481 0.019193 0.052357 0.833920 6.160000e+09 2.405602e-04 0.630569 0.048233
2009 0 0.231365 0.162778 0.998780 0.008515 0.088022 0.848051 3.860000e+09 2.605237e-04 0.630558 0.021436
1938 0 0.237459 0.176734 0.995222 0.001467 0.069538 0.847672 2.810000e+09 3.086857e-04 0.630612 0.002819
3540 0 0.160581 0.178805 0.998584 0.021522 0.057487 0.848008 6.700000e+09 2.653150e-04 0.630585 0.027471
1755 1 0.270365 0.183221 0.998093 0.013461 0.040795 0.847600 2.800000e+09 2.390630e-04 0.630514 0.037611
3695 0 0.216984 0.188127 0.996180 0.004885 0.031919 0.848003 9.790000e+09 1.052850e-03 0.630480 0.222178
3749 1 0.258665 0.190580 0.998971 0.006836 0.095513 0.847927 3.140000e+09 2.982548e-04 0.630586 0.006436
3224 0 0.211329 0.196195 0.994427 0.001286 0.067910 0.848022 7.520000e+09 4.771777e-04 0.630612 0.000653
1753 1 0.201969 0.200720 0.998625 0.041381 0.030535 0.846452 2.620000e+09 2.651403e-04 0.630583 0.017482
2293 1 0.240579 0.204481 0.998820 0.010814 0.088104 0.847173 1.750000e+08 2.615968e-04 0.630513 0.015423
6640 1 0.196802 0.211023 0.998933 0.025757 0.091931 0.847908 2.590000e+09 0.000000e+00 0.630536 0.021102
2470 1 0.404036 0.223615 0.998568 0.018361 0.061559 0.843895 4.120000e+09 9.330000e+09 0.630462 0.076939
3171 0 0.239458 0.233864 0.997059 0.001149 0.085009 0.848068 5.690000e+07 4.374503e-04 0.630576 0.003926
1865 1 0.207722 0.236862 0.998834 0.034439 0.074912 0.847459 5.140000e+09 2.041768e-04 0.630576 0.508690
6641 1 0.337640 0.254307 0.998869 0.049730 0.074098 0.847953 2.570000e+08 2.240654e-04 0.630567 0.085128
1688 0 0.249305 0.259376 0.997640 0.003645 0.072795 0.848001 3.561420e-04 1.390408e-03 0.630612 0.001737
5033 0 0.203968 0.263792 0.995945 0.003978 0.039329 0.846758 1.680725e-04 3.588440e-04 0.630514 0.039994
1686 0 0.269000 0.264555 0.973424 0.000151 0.077600 0.847526 1.588585e-04 6.457695e-04 0.630611 0.002043
5096 0 0.374007 0.264828 0.998626 0.006488 0.085091 0.848065 3.140000e+09 3.228848e-04 0.630571 0.003955

Verificar si a patir de la Operating Profit Growth Rate se oberva comportamiento de las empresas

In [ ]:
table3 = prueba1.sort_values('Operating Profit Growth Rate',ascending=False)
In [ ]:
table3.head(30)
Out[ ]:
Bankrupt? ROA(C) before interest and depreciation before interest ROA(A) before interest and % after tax Operating Profit Rate Revenue Per Share (Yuan ¥) Operating Profit Per Share (Yuan ¥) Operating Profit Growth Rate Total Asset Growth Rate Net Value Growth Rate Interest Expense Ratio Total debt/Total net worth
1575 0 0.538390 0.592619 0.998996 0.190299 0.126537 1.000000 5.663654e-04 0.001527 0.631110 0.027573
3238 0 0.502706 0.587713 0.999029 0.056415 0.113264 0.932216 3.078512e-04 0.000755 0.630678 0.007149
1810 0 0.603130 0.650076 0.999024 0.121966 0.131260 0.892423 9.433817e-04 0.012480 0.630724 0.010960
1004 0 0.502218 0.548572 0.998989 0.030733 0.099259 0.887704 5.540000e+09 0.000455 0.630922 0.004541
1309 0 0.485789 0.551788 0.999009 0.040126 0.104226 0.883912 8.830000e+09 0.000462 0.632269 0.018100
6283 0 0.555989 0.587604 0.999124 0.028858 0.117173 0.874452 7.280000e+09 0.000498 0.630774 0.004533
4951 0 0.490713 0.551188 0.999085 0.024018 0.109112 0.870802 5.910000e+09 0.000472 0.631381 0.005217
5550 0 0.481792 0.545083 0.998975 0.071268 0.099910 0.870257 6.980000e+09 0.000462 0.630678 0.008206
1852 0 0.520792 0.552660 0.999094 0.020736 0.108135 0.865672 8.400000e+09 0.000459 0.630972 0.005831
1514 0 0.485058 0.546010 0.998975 0.104285 0.102028 0.864264 1.011203e-04 0.000567 0.630860 0.012126
4877 0 0.971530 1.000000 0.999571 0.046887 0.228483 0.863892 2.114707e-04 0.001344 0.630641 0.003080
5315 0 0.575245 0.622492 0.999186 0.037963 0.135005 0.863707 1.310798e-04 0.000919 0.630770 0.008609
3182 0 0.434895 0.496784 0.999072 0.037267 0.114567 0.863109 6.810000e+09 0.000391 0.629733 0.026929
2318 0 0.533223 0.585369 0.999025 0.114494 0.129550 0.861741 7.750000e+09 0.000544 0.630796 0.020664
1884 0 0.528202 0.561873 0.999074 0.022566 0.107076 0.861678 1.085800e-04 0.000574 0.630992 0.010757
1911 0 0.548774 0.575992 0.999056 0.012342 0.100643 0.860268 1.079443e-04 0.000606 0.630612 0.001065
1362 0 0.489689 0.540722 0.998967 0.052694 0.096653 0.860259 6.560000e+09 0.000440 0.634986 0.013028
4838 0 0.501926 0.567488 0.999064 0.090446 0.138669 0.859772 2.170956e-04 0.001203 0.631092 0.006990
973 0 0.517623 0.594908 0.999046 0.054177 0.116684 0.859657 1.100607e-04 0.000493 0.630749 0.007182
5901 0 0.477063 0.540994 0.998967 0.014550 0.095595 0.859023 7.050000e+09 0.000504 0.631892 0.004083
3989 0 0.501584 0.549608 0.999006 0.016562 0.098689 0.858784 6.770000e+09 0.000446 0.630612 0.000984
2266 0 0.506996 0.572939 0.999210 0.025848 0.125234 0.858436 1.818572e-04 0.000576 0.631052 0.022247
5966 0 0.533418 0.585259 0.999046 0.020857 0.103493 0.858417 6.330000e+09 0.000589 0.631028 0.005934
4636 0 0.731829 0.805877 0.999554 0.043136 0.214478 0.858322 2.362236e-04 0.000783 0.630652 0.006577
501 0 0.500024 0.559038 0.999004 0.018467 0.098933 0.857528 7.140000e+08 0.000449 0.631178 0.007174
4178 0 0.502267 0.565471 0.998993 0.086347 0.108379 0.857521 7.210000e+09 0.000486 0.630672 0.010880
977 0 0.514259 0.563399 0.999018 0.040958 0.106099 0.856979 5.220000e+09 0.000472 0.630811 0.004901
3586 0 0.542827 0.567215 0.999070 0.013915 0.102272 0.856814 7.120000e+09 0.000465 0.630613 0.001356
4171 0 0.477600 0.547645 0.998974 0.134731 0.103493 0.856312 6.410000e+09 0.000460 0.630729 0.008298
6791 0 0.530932 0.560347 0.998991 0.022112 0.098282 0.856131 6.930000e+09 0.000453 0.630912 0.007148
In [ ]:
table4 = prueba1.sort_values('Operating Profit Growth Rate',ascending=True)
In [ ]:
table4.head(30)  # Al presentar la caracteristica Operating Profit Growth Rate con los primeros 30 más bajos, aparecen algunas empresas en categoría de Bankrupt? = 1
Out[ ]:
Bankrupt? ROA(C) before interest and depreciation before interest ROA(A) before interest and % after tax Operating Profit Rate Revenue Per Share (Yuan ¥) Operating Profit Per Share (Yuan ¥) Operating Profit Growth Rate Total Asset Growth Rate Net Value Growth Rate Interest Expense Ratio Total debt/Total net worth
4447 0 0.457368 0.527693 0.994709 0.000212 0.090872 0.000000 1.000000e+00 1.000000 0.626271 0.019031
2001 1 0.438795 0.090166 0.997789 0.003721 0.074912 0.736430 2.490000e+08 0.000167 0.630547 0.090127
337 0 0.458051 0.518153 0.998923 0.007456 0.093885 0.777860 5.490000e+09 0.000460 0.629639 0.006094
4476 0 0.474041 0.532054 0.998903 0.007638 0.093152 0.792087 6.400000e+09 0.000450 0.630612 0.000577
5436 0 0.390923 0.410379 0.998913 0.029387 0.088674 0.810684 4.150000e+09 0.000372 0.630607 0.005498
1132 1 0.433384 0.486372 0.998902 0.025969 0.088104 0.813643 5.780000e+09 0.000362 0.629644 0.035590
4294 0 0.387949 0.425207 0.998891 0.047658 0.079635 0.817521 4.050000e+09 0.000372 0.630348 0.009622
1630 0 0.352849 0.402257 0.998413 0.024245 0.033385 0.820571 4.740000e+09 0.000386 0.630612 0.001267
6785 0 0.471116 0.543229 0.998868 0.018815 0.087045 0.826846 7.630000e+09 0.000435 0.631341 0.007850
6275 0 0.491591 0.557894 0.998840 0.015866 0.086312 0.830726 6.880000e+09 0.000472 0.630612 0.001651
1598 0 0.425681 0.450556 0.998761 0.015715 0.080531 0.830776 4.860000e+09 0.000382 0.630312 0.006165
3845 0 0.301126 0.398877 0.995781 0.001981 0.065874 0.831638 4.244220e-04 0.000935 0.630522 0.007673
3952 0 0.454102 0.493622 0.998876 0.015790 0.088999 0.833147 6.370000e+08 0.000426 0.630612 0.001293
1951 1 0.208161 0.160870 0.998481 0.019193 0.052357 0.833920 6.160000e+09 0.000241 0.630569 0.048233
3848 0 0.356311 0.394134 0.998856 0.042546 0.074505 0.835647 1.247064e-04 0.000835 0.630612 0.007415
1639 1 0.438454 0.472307 0.998732 0.017908 0.076134 0.836882 5.560000e+09 0.000388 0.629974 0.010556
5054 0 0.445327 0.427987 0.998202 0.009166 0.062780 0.838373 4.040000e+09 0.000360 0.630402 0.007987
4066 0 0.438210 0.489370 0.998784 0.011117 0.086068 0.839253 6.220000e+09 0.000424 0.630533 0.003090
2544 0 0.452104 0.522841 0.998884 0.028934 0.084928 0.839617 7.490000e+09 0.000425 0.626299 0.009153
2536 1 0.441476 0.511666 0.998851 0.011707 0.089244 0.839645 6.750000e+09 0.000403 0.628055 0.006812
33 0 0.486374 0.544756 0.998944 0.025062 0.093233 0.841391 6.760000e+09 0.000452 0.631231 0.002449
239 0 0.434456 0.481247 0.998791 0.009755 0.087452 0.842429 5.620000e+09 0.000419 0.630611 0.001595
459 0 0.460196 0.512266 0.998937 0.008334 0.094292 0.842697 5.790000e+09 0.000432 0.630267 0.002840
6344 0 0.431629 0.497165 0.998929 0.227460 0.062291 0.843317 5.760000e+07 0.000370 0.630217 0.041999
3168 0 0.460440 0.520933 0.998791 0.003615 0.092338 0.843398 9.220000e+09 0.000502 0.630498 0.001037
3750 0 0.460098 0.504361 0.998883 0.012236 0.090791 0.843407 5.460000e+09 0.000431 0.630188 0.002647
3842 0 0.412129 0.488716 0.995496 0.002133 0.060663 0.843454 5.521059e-04 0.000374 0.629978 0.081796
2816 0 0.457222 0.497983 0.998902 0.010648 0.092256 0.843544 5.630000e+09 0.000404 0.629914 0.012228
3803 0 0.438697 0.481411 0.998883 0.035694 0.082322 0.843654 8.100000e+09 0.000384 0.630286 0.016475
1823 0 0.432847 0.504416 0.998929 0.098099 0.080775 0.843796 1.038966e-04 0.000417 0.630397 0.018493

Selección de Caracteristicas con algún Interes

In [ ]:
prueba2 = datos.iloc[:,[0,1,6,25,29,34,45,46,48,71,74,81,89,94]]   # Vamos a tomar algunas caracteristicas que representan ratios de Liquidez, Solvencia y  Rentabilidad, 
                                                                   # así como el indicador de la income flag
In [ ]:
prueba2.head(3)
Out[ ]:
Bankrupt? ROA(C) before interest and depreciation before interest Operating Profit Rate Operating Profit Growth Rate Total Asset Growth Rate Quick Ratio Total Asset Turnover Accounts Receivable Turnover Inventory Turnover Rate (times) Current Asset Turnover Rate Cash Turnover Rate Cash Flow to Liability Gross Profit to Sales Net Income Flag
0 1 0.370594 0.998969 0.848195 4.980000e+09 0.001208 0.086957 0.001814 1.820926e-04 7.010000e+08 4.580000e+08 0.458609 0.601453 1
1 1 0.464291 0.998946 0.848088 6.110000e+09 0.004039 0.064468 0.001286 9.360000e+09 1.065198e-04 2.490000e+09 0.459001 0.610237 1
2 1 0.426071 0.998857 0.848094 7.280000e+09 0.005348 0.014993 0.001495 6.500000e+07 1.791094e-03 7.610000e+08 0.459254 0.601449 1

Correlaciones:

Según nos indican las Correlaciones si son negativas o inversas, una sube y l aotra nisminuye, mientras que las positivas una sube y la otra también sube. Entre más cercano se encuentre a -1 o 1 la correlación es más fuerte, si se aceerca a cero 0 o da cero, indica que no son dependientes entre sí

In [ ]:
datos.corr()  # Vamos  a elegir variables o caracteristicas que permitan llegar a alguna conclusión y que sean relevantes
              # Bankrupt?,ROA(C) before interest and depreciation before interest,
Out[ ]:
Bankrupt? ROA(C) before interest and depreciation before interest ROA(A) before interest and % after tax ROA(B) before interest and depreciation after tax Operating Gross Margin Realized Sales Gross Margin Operating Profit Rate Pre-tax net Interest Rate After-tax net Interest Rate Non-industry income and expenditure/revenue Continuous interest rate (after tax) Operating Expense Rate Research and development expense rate Cash flow rate Interest-bearing debt interest rate Tax rate (A) Net Value Per Share (B) Net Value Per Share (A) Net Value Per Share (C) Persistent EPS in the Last Four Seasons Cash Flow Per Share Revenue Per Share (Yuan ¥) Operating Profit Per Share (Yuan ¥) Per Share Net profit before tax (Yuan ¥) Realized Sales Gross Profit Growth Rate Operating Profit Growth Rate After-tax Net Profit Growth Rate Regular Net Profit Growth Rate Continuous Net Profit Growth Rate Total Asset Growth Rate Net Value Growth Rate Total Asset Return Growth Rate Ratio Cash Reinvestment % Current Ratio Quick Ratio Interest Expense Ratio Total debt/Total net worth Debt ratio % Net worth/Assets Long-term fund suitability ratio (A) ... Current Assets/Total Assets Cash/Total Assets Quick Assets/Current Liability Cash/Current Liability Current Liability to Assets Operating Funds to Liability Inventory/Working Capital Inventory/Current Liability Current Liabilities/Liability Working Capital/Equity Current Liabilities/Equity Long-term Liability to Current Assets Retained Earnings to Total Assets Total income/Total expense Total expense/Assets Current Asset Turnover Rate Quick Asset Turnover Rate Working capitcal Turnover Rate Cash Turnover Rate Cash Flow to Sales Fixed Assets to Assets Current Liability to Liability Current Liability to Equity Equity to Long-term Liability Cash Flow to Total Assets Cash Flow to Liability CFO to Assets Cash Flow to Equity Current Liability to Current Assets Liability-Assets Flag Net Income to Total Assets Total assets to GNP price No-credit Interval Gross Profit to Sales Net Income to Stockholder's Equity Liability to Equity Degree of Financial Leverage (DFL) Interest Coverage Ratio (Interest expense to EBIT) Net Income Flag Equity to Liability
Bankrupt? 1.000000 -0.260807 -0.282941 -0.273051 -0.100043 -0.099445 -0.000230 -0.008517 -0.008857 -0.016593 -0.008395 -0.006083 -0.024232 -0.072356 -0.023063 -0.109706 -0.165399 -0.165465 -0.164784 -0.219560 -0.077516 -0.004692 -0.142051 -0.201395 -0.000458 -0.015168 -0.037783 -0.036820 -0.009401 -0.044431 0.065329 -0.016858 -0.051345 -0.002211 0.025058 -0.002681 0.012314 0.250161 -0.250161 0.016920 ... -0.044823 -0.100130 -0.003823 0.077921 0.194494 -0.077082 -0.001906 0.000822 -0.020809 -0.147221 0.153828 0.000778 -0.217779 -0.007137 0.139049 0.011929 0.025814 -0.002894 -0.018035 0.000479 0.066328 -0.020809 0.153828 0.139014 -0.070456 -0.043125 -0.115383 -0.058563 0.171306 0.139212 -0.315457 0.035104 -0.005547 -0.100044 -0.180987 0.166812 0.010508 -0.005509 NaN -0.083048
ROA(C) before interest and depreciation before interest -0.260807 1.000000 0.940124 0.986849 0.334719 0.332755 0.035725 0.053419 0.049222 0.020501 0.051328 0.066869 0.106461 0.323482 0.048882 0.250761 0.505580 0.505407 0.505281 0.775006 0.379839 -0.015932 0.687201 0.750564 0.000591 0.036511 0.115083 0.115040 0.025234 0.019635 -0.021930 0.079906 0.296158 0.013196 -0.026336 0.003988 -0.022208 -0.261427 0.261427 0.002967 ... 0.098820 0.235314 -0.010530 -0.046009 -0.210256 0.388151 -0.004447 0.013330 0.052783 0.103819 -0.142734 0.021508 0.650217 0.023450 -0.296019 0.005716 -0.027280 0.001824 -0.029477 0.011759 -0.009192 0.052783 -0.142734 -0.086535 0.262454 0.159699 0.504311 0.129002 -0.160725 -0.109272 0.887670 -0.071725 0.008135 0.334721 0.274287 -0.143629 -0.016575 0.010573 NaN 0.052416
ROA(A) before interest and % after tax -0.282941 0.940124 1.000000 0.955741 0.326969 0.324956 0.032053 0.053518 0.049474 0.029649 0.049909 0.075727 0.084334 0.288440 0.050362 0.225897 0.531799 0.531790 0.531821 0.764828 0.326239 -0.011829 0.654253 0.752578 0.003277 0.042208 0.125384 0.125872 0.024887 0.026977 -0.063970 0.081982 0.263615 0.014102 -0.018412 0.005440 -0.010323 -0.259972 0.259972 0.020707 ... 0.157005 0.217918 -0.009612 -0.037468 -0.190501 0.351107 -0.000004 0.004864 0.080401 0.120403 -0.133816 0.022241 0.718013 0.028873 -0.357147 -0.000869 -0.025143 0.004491 -0.025817 0.012198 -0.005860 0.080401 -0.133816 -0.103015 0.263591 0.157065 0.443017 0.112929 -0.195673 -0.156890 0.961552 -0.098900 0.011463 0.326971 0.291744 -0.141039 -0.011515 0.013372 NaN 0.057887
ROA(B) before interest and depreciation after tax -0.273051 0.986849 0.955741 1.000000 0.333749 0.331755 0.035212 0.053726 0.049952 0.022366 0.052261 0.065602 0.102147 0.323040 0.045839 0.197344 0.502052 0.502000 0.501907 0.764597 0.366216 -0.014359 0.659834 0.722940 0.002142 0.036144 0.117130 0.117042 0.024414 0.022104 -0.026127 0.079972 0.292008 0.012975 -0.024232 0.005187 -0.021161 -0.264734 0.264734 0.003869 ... 0.094083 0.227144 -0.010014 -0.041296 -0.217186 0.387893 -0.001616 0.007302 0.046694 0.101962 -0.142879 0.018300 0.673738 0.024436 -0.322223 -0.002611 -0.029928 0.002488 -0.030410 0.011977 -0.008364 0.046694 -0.142879 -0.083190 0.258428 0.157022 0.497042 0.123622 -0.162572 -0.120680 0.912040 -0.089088 0.007523 0.333750 0.280617 -0.142838 -0.014663 0.011473 NaN 0.056430
Operating Gross Margin -0.100043 0.334719 0.326969 0.333749 1.000000 0.999518 0.005745 0.032493 0.027175 0.051438 0.029430 -0.206353 -0.016976 0.341188 0.017198 0.067970 0.144661 0.145031 0.145057 0.256722 0.163192 0.117045 0.267944 0.247789 0.014172 0.022867 0.054639 0.053430 0.009121 0.016013 -0.017448 0.026545 0.122676 0.024945 0.001379 -0.002366 -0.022360 -0.245460 0.245460 0.006020 ... 0.094782 0.241946 -0.003206 -0.030901 -0.198027 0.246834 -0.035025 0.035218 0.063547 0.067970 -0.080422 0.000522 0.164579 0.043608 0.225479 -0.121275 -0.129715 0.020451 -0.071579 -0.041559 0.003507 0.063547 -0.080422 -0.068810 0.098097 0.114138 0.226990 0.030672 -0.132650 -0.032930 0.300143 0.022672 0.004205 1.000000 0.075304 -0.085434 -0.011806 -0.001167 NaN 0.120029
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Liability to Equity 0.166812 -0.143629 -0.141039 -0.142838 -0.085434 -0.085407 0.001541 -0.004043 -0.004390 -0.011899 -0.002996 0.034809 -0.035363 -0.080773 -0.003423 -0.030002 -0.110850 -0.111797 -0.111682 -0.114114 -0.047298 -0.002132 -0.077102 -0.107727 0.001687 0.000537 -0.011685 -0.011705 -0.007433 -0.033052 -0.068649 -0.005198 -0.133686 -0.003741 0.009645 0.006926 -0.010045 0.349250 -0.349250 0.001791 ... 0.039449 -0.097849 -0.061051 0.029935 0.286398 -0.091214 0.001788 0.007637 -0.017927 -0.650474 0.963908 0.000063 -0.109810 -0.007383 0.050501 0.043730 0.068305 -0.007808 0.013463 -0.003770 0.010509 -0.017927 0.963908 0.778135 -0.015893 -0.019213 -0.098545 -0.231107 0.132372 -0.229559 -0.159697 0.021982 -0.003724 -0.085434 -0.791836 1.000000 0.002119 0.001487 NaN -0.159654
Degree of Financial Leverage (DFL) 0.010508 -0.016575 -0.011515 -0.014663 -0.011806 -0.011268 0.000935 0.000855 0.000927 -0.000556 0.000774 0.013577 -0.013945 -0.006348 -0.007301 -0.014962 -0.021860 -0.021781 -0.021674 -0.018829 -0.006200 -0.001140 -0.015936 -0.017885 -0.000672 0.001247 0.002030 0.002014 0.000014 0.005520 -0.000697 -0.000310 0.003717 -0.000574 -0.000083 0.016829 -0.001262 0.017982 -0.017982 -0.004319 ... -0.031049 -0.024289 -0.000876 0.001636 0.006987 -0.008961 0.002294 -0.000344 -0.017911 -0.011134 0.000745 -0.002160 -0.013766 -0.001156 -0.017607 -0.003415 0.001264 -0.000342 0.000143 0.000208 -0.000825 -0.017911 0.000745 0.002936 -0.002991 -0.002400 -0.003771 -0.001471 0.022033 -0.001717 -0.010463 -0.001881 -0.008812 -0.011806 -0.000093 0.002119 1.000000 0.016513 NaN -0.016739
Interest Coverage Ratio (Interest expense to EBIT) -0.005509 0.010573 0.013372 0.011473 -0.001167 -0.001158 0.000393 0.000984 0.000957 0.001024 0.000798 0.006232 -0.012160 0.001262 -0.000779 0.030275 -0.002175 -0.002358 -0.002328 0.008039 0.001358 -0.000053 0.006331 0.008143 -0.000327 0.004576 0.005373 0.005329 0.001086 0.001723 -0.000446 0.001498 0.005525 -0.000150 0.001867 -0.034321 -0.000431 0.012571 -0.012571 -0.000342 ... 0.007281 -0.007205 0.000209 0.003422 0.021428 -0.001268 0.023608 0.005260 0.009450 -0.002063 0.005038 0.003908 0.009603 0.000088 -0.005122 0.025886 -0.009416 -0.000004 -0.003773 -0.000083 -0.002169 0.009450 0.005038 -0.007973 0.000176 0.001836 0.006057 0.000239 0.007652 -0.000974 0.012746 0.000239 0.001027 -0.001169 0.005147 0.001487 0.016513 1.000000 NaN -0.008339
Net Income Flag NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Equity to Liability -0.083048 0.052416 0.057887 0.056430 0.120029 0.120196 -0.017071 -0.014559 -0.010900 0.012293 -0.011299 -0.120763 -0.045244 0.331710 0.028945 -0.053148 0.098434 0.098721 0.098390 0.036722 0.052117 0.233203 -0.009316 0.028185 -0.002302 0.001725 0.001253 0.001383 0.014498 -0.015962 -0.010685 -0.002454 0.045546 0.010228 -0.017084 -0.012626 0.338898 -0.625879 0.625879 0.104344 ... -0.015311 0.299732 -0.012449 -0.038004 -0.506360 0.398769 -0.005719 0.004448 0.098930 0.058512 -0.156443 0.014437 0.042936 0.031325 0.007907 -0.101157 -0.156954 0.077424 -0.145710 0.022476 -0.007758 0.098930 -0.156443 -0.110885 0.034015 0.109775 0.113629 0.003321 -0.262199 -0.027573 0.073916 0.014871 0.050609 0.120027 0.029622 -0.159654 -0.016739 -0.008339 NaN 1.000000

96 rows × 96 columns

Según nos indican las Correlaciones si son negativas o inversas, una sube y l aotra nisminuye, mientras que las positivas una sube y la otra también sube. Entre más cercano se encuentre a -1 o 1 la correlación es más fuerte, si se aceerca a cero 0 o da cero, indica que no son dependientes entre sí

In [ ]:
cor_matrix = datos.corr().abs()   # esta versión permite colorear aquellas correlaciones que nos llaman la atención tanto positivas como negativas
cor_matrix.style.background_gradient(sns.light_palette('red', as_cmap=True))   # código tomado de la web en que aplican este método, es muy útil ayuda cuando hay muchas variables
Out[ ]:
Bankrupt? ROA(C) before interest and depreciation before interest ROA(A) before interest and % after tax ROA(B) before interest and depreciation after tax Operating Gross Margin Realized Sales Gross Margin Operating Profit Rate Pre-tax net Interest Rate After-tax net Interest Rate Non-industry income and expenditure/revenue Continuous interest rate (after tax) Operating Expense Rate Research and development expense rate Cash flow rate Interest-bearing debt interest rate Tax rate (A) Net Value Per Share (B) Net Value Per Share (A) Net Value Per Share (C) Persistent EPS in the Last Four Seasons Cash Flow Per Share Revenue Per Share (Yuan ¥) Operating Profit Per Share (Yuan ¥) Per Share Net profit before tax (Yuan ¥) Realized Sales Gross Profit Growth Rate Operating Profit Growth Rate After-tax Net Profit Growth Rate Regular Net Profit Growth Rate Continuous Net Profit Growth Rate Total Asset Growth Rate Net Value Growth Rate Total Asset Return Growth Rate Ratio Cash Reinvestment % Current Ratio Quick Ratio Interest Expense Ratio Total debt/Total net worth Debt ratio % Net worth/Assets Long-term fund suitability ratio (A) Borrowing dependency Contingent liabilities/Net worth Operating profit/Paid-in capital Net profit before tax/Paid-in capital Inventory and accounts receivable/Net value Total Asset Turnover Accounts Receivable Turnover Average Collection Days Inventory Turnover Rate (times) Fixed Assets Turnover Frequency Net Worth Turnover Rate (times) Revenue per person Operating profit per person Allocation rate per person Working Capital to Total Assets Quick Assets/Total Assets Current Assets/Total Assets Cash/Total Assets Quick Assets/Current Liability Cash/Current Liability Current Liability to Assets Operating Funds to Liability Inventory/Working Capital Inventory/Current Liability Current Liabilities/Liability Working Capital/Equity Current Liabilities/Equity Long-term Liability to Current Assets Retained Earnings to Total Assets Total income/Total expense Total expense/Assets Current Asset Turnover Rate Quick Asset Turnover Rate Working capitcal Turnover Rate Cash Turnover Rate Cash Flow to Sales Fixed Assets to Assets Current Liability to Liability Current Liability to Equity Equity to Long-term Liability Cash Flow to Total Assets Cash Flow to Liability CFO to Assets Cash Flow to Equity Current Liability to Current Assets Liability-Assets Flag Net Income to Total Assets Total assets to GNP price No-credit Interval Gross Profit to Sales Net Income to Stockholder's Equity Liability to Equity Degree of Financial Leverage (DFL) Interest Coverage Ratio (Interest expense to EBIT) Net Income Flag Equity to Liability
Bankrupt? 1.000000 0.260807 0.282941 0.273051 0.100043 0.099445 0.000230 0.008517 0.008857 0.016593 0.008395 0.006083 0.024232 0.072356 0.023063 0.109706 0.165399 0.165465 0.164784 0.219560 0.077516 0.004692 0.142051 0.201395 0.000458 0.015168 0.037783 0.036820 0.009401 0.044431 0.065329 0.016858 0.051345 0.002211 0.025058 0.002681 0.012314 0.250161 0.250161 0.016920 0.176543 0.070455 0.141111 0.207857 0.075278 0.067915 0.004754 0.006556 0.001376 0.072818 0.021089 0.039718 0.092842 0.002829 0.193083 0.086382 0.044823 0.100130 0.003823 0.077921 0.194494 0.077082 0.001906 0.000822 0.020809 0.147221 0.153828 0.000778 0.217779 0.007137 0.139049 0.011929 0.025814 0.002894 0.018035 0.000479 0.066328 0.020809 0.153828 0.139014 0.070456 0.043125 0.115383 0.058563 0.171306 0.139212 0.315457 0.035104 0.005547 0.100044 0.180987 0.166812 0.010508 0.005509 nan 0.083048
ROA(C) before interest and depreciation before interest 0.260807 1.000000 0.940124 0.986849 0.334719 0.332755 0.035725 0.053419 0.049222 0.020501 0.051328 0.066869 0.106461 0.323482 0.048882 0.250761 0.505580 0.505407 0.505281 0.775006 0.379839 0.015932 0.687201 0.750564 0.000591 0.036511 0.115083 0.115040 0.025234 0.019635 0.021930 0.079906 0.296158 0.013196 0.026336 0.003988 0.022208 0.261427 0.261427 0.002967 0.161671 0.035729 0.685028 0.753339 0.109888 0.210622 0.033947 0.007019 0.062660 0.065919 0.022896 0.014834 0.301996 0.012543 0.259680 0.181993 0.098820 0.235314 0.010530 0.046009 0.210256 0.388151 0.004447 0.013330 0.052783 0.103819 0.142734 0.021508 0.650217 0.023450 0.296019 0.005716 0.027280 0.001824 0.029477 0.011759 0.009192 0.052783 0.142734 0.086535 0.262454 0.159699 0.504311 0.129002 0.160725 0.109272 0.887670 0.071725 0.008135 0.334721 0.274287 0.143629 0.016575 0.010573 nan 0.052416
ROA(A) before interest and % after tax 0.282941 0.940124 1.000000 0.955741 0.326969 0.324956 0.032053 0.053518 0.049474 0.029649 0.049909 0.075727 0.084334 0.288440 0.050362 0.225897 0.531799 0.531790 0.531821 0.764828 0.326239 0.011829 0.654253 0.752578 0.003277 0.042208 0.125384 0.125872 0.024887 0.026977 0.063970 0.081982 0.263615 0.014102 0.018412 0.005440 0.010323 0.259972 0.259972 0.020707 0.161868 0.036183 0.651581 0.758234 0.078585 0.223528 0.031262 0.009041 0.054496 0.136964 0.036925 0.014888 0.324942 0.006035 0.303532 0.202017 0.157005 0.217918 0.009612 0.037468 0.190501 0.351107 0.000004 0.004864 0.080401 0.120403 0.133816 0.022241 0.718013 0.028873 0.357147 0.000869 0.025143 0.004491 0.025817 0.012198 0.005860 0.080401 0.133816 0.103015 0.263591 0.157065 0.443017 0.112929 0.195673 0.156890 0.961552 0.098900 0.011463 0.326971 0.291744 0.141039 0.011515 0.013372 nan 0.057887
ROA(B) before interest and depreciation after tax 0.273051 0.986849 0.955741 1.000000 0.333749 0.331755 0.035212 0.053726 0.049952 0.022366 0.052261 0.065602 0.102147 0.323040 0.045839 0.197344 0.502052 0.502000 0.501907 0.764597 0.366216 0.014359 0.659834 0.722940 0.002142 0.036144 0.117130 0.117042 0.024414 0.022104 0.026127 0.079972 0.292008 0.012975 0.024232 0.005187 0.021161 0.264734 0.264734 0.003869 0.158618 0.034177 0.657274 0.726003 0.109501 0.194810 0.033768 0.009921 0.053605 0.061046 0.012763 0.014545 0.304522 0.012770 0.260151 0.166311 0.094083 0.227144 0.010014 0.041296 0.217186 0.387893 0.001616 0.007302 0.046694 0.101962 0.142879 0.018300 0.673738 0.024436 0.322223 0.002611 0.029928 0.002488 0.030410 0.011977 0.008364 0.046694 0.142879 0.083190 0.258428 0.157022 0.497042 0.123622 0.162572 0.120680 0.912040 0.089088 0.007523 0.333750 0.280617 0.142838 0.014663 0.011473 nan 0.056430
Operating Gross Margin 0.100043 0.334719 0.326969 0.333749 1.000000 0.999518 0.005745 0.032493 0.027175 0.051438 0.029430 0.206353 0.016976 0.341188 0.017198 0.067970 0.144661 0.145031 0.145057 0.256722 0.163192 0.117045 0.267944 0.247789 0.014172 0.022867 0.054639 0.053430 0.009121 0.016013 0.017448 0.026545 0.122676 0.024945 0.001379 0.002366 0.022360 0.245460 0.245460 0.006020 0.085733 0.022258 0.267411 0.248104 0.086720 0.099661 0.082342 0.022530 0.047665 0.001239 0.136157 0.019022 0.224976 0.006953 0.246304 0.152850 0.094782 0.241946 0.003206 0.030901 0.198027 0.246834 0.035025 0.035218 0.063547 0.067970 0.080422 0.000522 0.164579 0.043608 0.225479 0.121275 0.129715 0.020451 0.071579 0.041559 0.003507 0.063547 0.080422 0.068810 0.098097 0.114138 0.226990 0.030672 0.132650 0.032930 0.300143 0.022672 0.004205 1.000000 0.075304 0.085434 0.011806 0.001167 nan 0.120029
Realized Sales Gross Margin 0.099445 0.332755 0.324956 0.331755 0.999518 1.000000 0.005610 0.032232 0.026851 0.051242 0.029166 0.206439 0.017391 0.341433 0.017121 0.067708 0.142887 0.143262 0.143288 0.254753 0.163163 0.117196 0.267021 0.246004 0.014188 0.022778 0.054470 0.053259 0.009117 0.016583 0.017451 0.026463 0.122787 0.024984 0.001418 0.002509 0.022354 0.245606 0.245606 0.006189 0.085598 0.022239 0.266483 0.246256 0.086762 0.100141 0.082479 0.022596 0.047079 0.001289 0.136335 0.019060 0.224458 0.007262 0.246221 0.152805 0.094838 0.242353 0.003186 0.031045 0.197842 0.246781 0.035085 0.035478 0.064657 0.067921 0.080350 0.000178 0.163013 0.043610 0.226170 0.121320 0.129747 0.020536 0.071321 0.041604 0.003524 0.064657 0.080350 0.068763 0.098056 0.114060 0.226912 0.030676 0.132607 0.032920 0.298155 0.022750 0.004038 0.999518 0.074891 0.085407 0.011268 0.001158 nan 0.120196
Operating Profit Rate 0.000230 0.035725 0.032053 0.035212 0.005745 0.005610 1.000000 0.916448 0.862191 0.592006 0.915544 0.013246 0.016387 0.023051 0.002784 0.019936 0.019257 0.019218 0.019240 0.020420 0.014244 0.044460 0.022397 0.020219 0.000831 0.004952 0.011328 0.011227 0.001318 0.034465 0.000207 0.003677 0.014955 0.000833 0.000323 0.001156 0.001507 0.010397 0.010397 0.000833 0.001092 0.000247 0.022160 0.020015 0.011993 0.029456 0.023171 0.001001 0.009576 0.005232 0.016064 0.027450 0.018248 0.000726 0.025599 0.026100 0.033821 0.010465 0.000365 0.000301 0.011340 0.020308 0.001026 0.001748 0.020520 0.010600 0.001860 0.001967 0.021280 0.002047 0.005401 0.008117 0.012696 0.229568 0.016485 0.084747 0.000106 0.020520 0.001860 0.000654 0.020918 0.004669 0.026682 0.014088 0.079679 0.000295 0.028482 0.003338 0.000199 0.005746 0.006216 0.001541 0.000935 0.000393 nan 0.017071
Pre-tax net Interest Rate 0.008517 0.053419 0.053518 0.053726 0.032493 0.032232 0.916448 1.000000 0.986379 0.220045 0.993617 0.014247 0.016836 0.024950 0.004031 0.023003 0.033034 0.033015 0.033035 0.033726 0.017617 0.004931 0.026314 0.034046 0.001246 0.003909 0.035150 0.034914 0.003013 0.037633 0.000998 0.005004 0.017801 0.003164 0.017376 0.001630 0.001964 0.003906 0.003906 0.001907 0.004654 0.002222 0.026091 0.033900 0.009042 0.029667 0.089576 0.002198 0.005560 0.003581 0.015513 0.144956 0.020271 0.000685 0.036347 0.030819 0.037156 0.017136 0.000296 0.001404 0.001632 0.022855 0.010231 0.017489 0.019009 0.014933 0.002202 0.002062 0.036236 0.003322 0.004525 0.008065 0.012206 0.090689 0.015581 0.233675 0.000047 0.019009 0.002202 0.007929 0.041845 0.011517 0.031813 0.026245 0.138584 0.003163 0.048587 0.004243 0.000075 0.032494 0.011343 0.004043 0.000855 0.000984 nan 0.014559
After-tax net Interest Rate 0.008857 0.049222 0.049474 0.049952 0.027175 0.026851 0.862191 0.986379 1.000000 0.115211 0.984452 0.013982 0.016521 0.022813 0.003824 0.021164 0.031369 0.031347 0.031367 0.030768 0.016140 0.005594 0.024137 0.030621 0.001226 0.002962 0.031223 0.030964 0.002565 0.037066 0.000858 0.004379 0.016692 0.002685 0.015369 0.001612 0.001379 0.006174 0.006174 0.001227 0.004395 0.002117 0.023942 0.030568 0.008993 0.029504 0.079920 0.002032 0.005957 0.004241 0.015736 0.146671 0.018334 0.000702 0.040405 0.031453 0.037836 0.017118 0.000343 0.000827 0.002805 0.020877 0.009501 0.015766 0.013732 0.016788 0.003196 0.002061 0.034573 0.002985 0.002803 0.008174 0.012368 0.244911 0.015792 0.379952 0.000003 0.013732 0.003196 0.006326 0.046314 0.012243 0.029454 0.030022 0.166453 0.002746 0.045390 0.003786 0.001091 0.027176 0.010648 0.004390 0.000927 0.000957 nan 0.010900
Non-industry income and expenditure/revenue 0.016593 0.020501 0.029649 0.022366 0.051438 0.051242 0.592006 0.220045 0.115211 1.000000 0.230698 0.003597 0.006041 0.005943 0.001332 0.002270 0.019588 0.019644 0.019632 0.018148 0.000758 0.118316 0.001601 0.019279 0.000484 0.004200 0.043179 0.042951 0.002855 0.008224 0.001505 0.001115 0.000606 0.004344 0.035784 0.000465 0.000284 0.033214 0.033214 0.001809 0.012037 0.003873 0.001473 0.019483 0.011027 0.012057 0.236897 0.001987 0.012145 0.005540 0.007916 0.225032 0.003658 0.000390 0.010800 0.001557 0.007613 0.009002 0.000294 0.003560 0.024357 0.003476 0.023107 0.030963 0.011739 0.004236 0.008969 0.000641 0.021105 0.001701 0.022281 0.003545 0.006365 0.742290 0.008805 0.677230 0.000355 0.011739 0.008969 0.014377 0.033285 0.011813 0.000973 0.018515 0.084875 0.005652 0.028423 0.000408 0.000637 0.051437 0.007693 0.011899 0.000556 0.001024 nan 0.012293
Continuous interest rate (after tax) 0.008395 0.051328 0.049909 0.052261 0.029430 0.029166 0.915544 0.993617 0.984452 0.230698 1.000000 0.013168 0.015728 0.027730 0.003654 0.020407 0.030839 0.030835 0.030840 0.032051 0.016343 0.051607 0.024516 0.030487 0.001207 0.002643 0.016584 0.016415 0.001842 0.034962 0.000354 0.004203 0.017400 0.002887 0.016857 0.001479 0.000075 0.001192 0.001192 0.000187 0.002887 0.002166 0.024295 0.030334 0.008243 0.027711 0.100604 0.002168 0.006203 0.003439 0.014559 0.064693 0.018580 0.000652 0.035148 0.028547 0.034865 0.016445 0.000300 0.001112 0.000159 0.024520 0.011909 0.005399 0.014602 0.014826 0.002689 0.001882 0.034777 0.003094 0.004472 0.007518 0.011440 0.110703 0.014596 0.254886 0.000016 0.014602 0.002689 0.003093 0.042328 0.014059 0.030653 0.027140 0.140264 0.001233 0.045600 0.004623 0.000556 0.029431 0.011191 0.002996 0.000774 0.000798 nan 0.011299
Operating Expense Rate 0.006083 0.066869 0.075727 0.065602 0.206353 0.206439 0.013246 0.014247 0.013982 0.003597 0.013168 1.000000 0.060386 0.020147 0.006011 0.060683 0.090519 0.091263 0.091197 0.080969 0.007253 0.015838 0.071799 0.081428 0.008170 0.013374 0.007176 0.009511 0.006644 0.014168 0.008456 0.003863 0.003016 0.007464 0.017687 0.024446 0.016164 0.143833 0.143833 0.008990 0.023977 0.010618 0.071972 0.082923 0.079747 0.195063 0.028331 0.007935 0.129214 0.055160 0.165135 0.010492 0.126869 0.009231 0.076724 0.004686 0.025720 0.110605 0.012904 0.024258 0.135256 0.042396 0.008018 0.011448 0.013653 0.007324 0.035899 0.001729 0.083315 0.001955 0.249426 0.170776 0.153936 0.003331 0.040730 0.003082 0.007464 0.013653 0.035899 0.024837 0.007630 0.006762 0.005426 0.014722 0.015511 0.004119 0.071365 0.025524 0.006497 0.206354 0.029733 0.034809 0.013577 0.006232 nan 0.120763
Research and development expense rate 0.024232 0.106461 0.084334 0.102147 0.016976 0.017391 0.016387 0.016836 0.016521 0.006041 0.015728 0.060386 1.000000 0.030918 0.000656 0.019201 0.088822 0.087500 0.087063 0.076486 0.052162 0.019291 0.068738 0.066085 0.011151 0.012166 0.019958 0.020703 0.007842 0.023189 0.010300 0.029752 0.042269 0.009092 0.025702 0.013572 0.019292 0.045162 0.045162 0.047014 0.045529 0.012449 0.067143 0.069545 0.047222 0.013498 0.034508 0.028471 0.001366 0.016872 0.025440 0.012780 0.043509 0.028073 0.011881 0.064811 0.022853 0.013524 0.015717 0.042133 0.046075 0.035521 0.015522 0.023691 0.007120 0.005879 0.037452 0.008636 0.079153 0.008301 0.049438 0.046460 0.034643 0.002909 0.070369 0.003677 0.009092 0.007120 0.037452 0.012524 0.008293 0.008771 0.073629 0.008972 0.065204 0.009960 0.079169 0.020166 0.006838 0.016975 0.021490 0.035363 0.013945 0.012160 nan 0.045244
Cash flow rate 0.072356 0.323482 0.288440 0.323040 0.341188 0.341433 0.023051 0.024950 0.022813 0.005943 0.027730 0.020147 0.030918 1.000000 0.011986 0.049835 0.158471 0.158520 0.158255 0.197705 0.353883 0.201679 0.191974 0.177008 0.017070 0.003731 0.019071 0.018300 0.003902 0.055389 0.007420 0.002779 0.344660 0.002257 0.012833 0.000513 0.349639 0.285010 0.285010 0.038308 0.078763 0.009771 0.191723 0.186619 0.125484 0.057978 0.005904 0.006638 0.020141 0.052978 0.096663 0.009018 0.131304 0.001554 0.161520 0.031135 0.049246 0.228027 0.008364 0.023394 0.278218 0.880562 0.006801 0.019862 0.068729 0.022609 0.087567 0.024446 0.223252 0.026204 0.081517 0.007870 0.057385 0.044478 0.093887 0.016086 0.006933 0.068729 0.087567 0.040178 0.224786 0.364812 0.603305 0.097761 0.126473 0.013501 0.281309 0.052766 0.013642 0.341186 0.057933 0.080773 0.006348 0.001262 nan 0.331710
Interest-bearing debt interest rate 0.023063 0.048882 0.050362 0.045839 0.017198 0.017121 0.002784 0.004031 0.003824 0.001332 0.003654 0.006011 0.000656 0.011986 1.000000 0.010080 0.050347 0.050345 0.050159 0.059506 0.015297 0.003904 0.018363 0.061431 0.003299 0.001783 0.010106 0.010021 0.000499 0.018700 0.013614 0.004281 0.000198 0.001840 0.005201 0.004360 0.003984 0.059911 0.059911 0.002245 0.001729 0.002792 0.016198 0.054972 0.035486 0.008551 0.006983 0.016496 0.007713 0.036213 0.046759 0.002586 0.004588 0.024254 0.045141 0.025246 0.013544 0.014468 0.055143 0.011063 0.041390 0.015720 0.002820 0.011240 0.025009 0.068273 0.018880 0.006090 0.023919 0.000202 0.010339 0.009759 0.026821 0.000607 0.019243 0.000173 0.001840 0.025009 0.018880 0.038229 0.012599 0.011341 0.011482 0.006995 0.000022 0.033571 0.048735 0.007519 0.003175 0.017198 0.010950 0.003423 0.007301 0.000779 nan 0.028945
Tax rate (A) 0.109706 0.250761 0.225897 0.197344 0.067970 0.067708 0.019936 0.023003 0.021164 0.002270 0.020407 0.060683 0.019201 0.049835 0.010080 1.000000 0.129891 0.130495 0.130665 0.169345 0.068363 0.021313 0.197467 0.208763 0.001507 0.018784 0.047621 0.047701 0.016857 0.067886 0.011380 0.009976 0.065329 0.000010 0.024875 0.034656 0.010497 0.009724 0.009724 0.012952 0.053177 0.012504 0.197419 0.209898 0.027022 0.193636 0.019968 0.000443 0.050769 0.135210 0.115270 0.014119 0.067826 0.005542 0.056103 0.119155 0.084386 0.011758 0.009854 0.035265 0.038520 0.051844 0.008833 0.017662 0.064182 0.041038 0.015970 0.016004 0.212204 0.000944 0.081476 0.063944 0.046586 0.002915 0.044051 0.004227 0.010045 0.064182 0.015970 0.054426 0.030302 0.023895 0.103101 0.021563 0.053579 0.028425 0.231210 0.023643 0.011488 0.067971 0.077920 0.030002 0.014962 0.030275 nan 0.053148
Net Value Per Share (B) 0.165399 0.505580 0.531799 0.502052 0.144661 0.142887 0.019257 0.033034 0.031369 0.019588 0.030839 0.090519 0.088822 0.158471 0.050347 0.129891 1.000000 0.999342 0.999179 0.755568 0.346904 0.008235 0.607623 0.726321 0.013744 0.016049 0.056818 0.056518 0.043865 0.018871 0.030175 0.060968 0.090651 0.010462 0.002909 0.008175 0.008546 0.249146 0.249146 0.052254 0.123991 0.028406 0.603024 0.706646 0.089396 0.082026 0.018647 0.002448 0.080684 0.080971 0.032829 0.017799 0.261330 0.009322 0.198620 0.115516 0.047254 0.185621 0.014827 0.033078 0.198546 0.195965 0.005685 0.015542 0.044689 0.070112 0.102098 0.011322 0.491365 0.023143 0.235573 0.017123 0.043038 0.006438 0.054775 0.009424 0.003112 0.044689 0.102098 0.089004 0.142925 0.076980 0.230814 0.072982 0.164367 0.096776 0.493776 0.059970 0.014303 0.144662 0.148693 0.110850 0.021860 0.002175 nan 0.098434
Net Value Per Share (A) 0.165465 0.505407 0.531790 0.502000 0.145031 0.143262 0.019218 0.033015 0.031347 0.019644 0.030835 0.091263 0.087500 0.158520 0.050345 0.130495 0.999342 1.000000 0.999837 0.755409 0.346511 0.008193 0.606954 0.725956 0.013689 0.016014 0.056679 0.056375 0.043767 0.018650 0.030089 0.060835 0.090377 0.010446 0.002874 0.008085 0.008547 0.249925 0.249925 0.052245 0.125321 0.028915 0.602367 0.705800 0.089992 0.082674 0.019675 0.002411 0.080248 0.081773 0.032440 0.017740 0.261381 0.009268 0.199598 0.115606 0.047816 0.185631 0.014773 0.032756 0.199086 0.195903 0.005589 0.015540 0.044624 0.070331 0.102539 0.011623 0.492760 0.023136 0.234878 0.016616 0.042926 0.006426 0.053797 0.009398 0.003094 0.044624 0.102539 0.090725 0.142930 0.076917 0.230629 0.073000 0.165083 0.096506 0.493803 0.059780 0.014424 0.145032 0.148872 0.111797 0.021781 0.002358 nan 0.098721
Net Value Per Share (C) 0.164784 0.505281 0.531821 0.501907 0.145057 0.143288 0.019240 0.033035 0.031367 0.019632 0.030840 0.091197 0.087063 0.158255 0.050159 0.130665 0.999179 0.999837 1.000000 0.755217 0.346243 0.008222 0.606895 0.725825 0.013708 0.016022 0.056682 0.056378 0.043751 0.018389 0.030099 0.060802 0.090312 0.010429 0.002013 0.008056 0.008514 0.249463 0.249463 0.052137 0.125188 0.028923 0.602277 0.705621 0.089777 0.082434 0.019724 0.002455 0.080054 0.081749 0.032455 0.017757 0.261477 0.009310 0.199475 0.115310 0.047968 0.185300 0.014794 0.031013 0.198721 0.195621 0.005580 0.015649 0.044538 0.070383 0.102431 0.011509 0.492734 0.023117 0.235062 0.016690 0.043121 0.006431 0.053930 0.009403 0.003107 0.044538 0.102431 0.090667 0.143017 0.076871 0.230342 0.073080 0.165011 0.096527 0.493822 0.059826 0.014335 0.145058 0.148906 0.111682 0.021674 0.002328 nan 0.098390
Persistent EPS in the Last Four Seasons 0.219560 0.775006 0.764828 0.764597 0.256722 0.254753 0.020420 0.033726 0.030768 0.018148 0.032051 0.080969 0.076486 0.197705 0.059506 0.169345 0.755568 0.755409 0.755217 1.000000 0.455794 0.009690 0.876769 0.955591 0.002178 0.025088 0.086080 0.086083 0.023707 0.036743 0.022101 0.080140 0.165224 0.011286 0.004244 0.000007 0.011383 0.177429 0.177429 0.052178 0.144138 0.053962 0.873641 0.959461 0.037986 0.214710 0.019997 0.007838 0.071460 0.129457 0.066033 0.011412 0.351589 0.009403 0.253188 0.215097 0.176931 0.240956 0.006744 0.034404 0.097689 0.248241 0.000283 0.031821 0.107310 0.121854 0.094966 0.019496 0.492078 0.023013 0.177996 0.000412 0.029352 0.002192 0.034256 0.007255 0.006477 0.107310 0.094966 0.114381 0.222378 0.123403 0.333636 0.129661 0.154690 0.105522 0.691152 0.033509 0.003791 0.256723 0.222961 0.114114 0.018829 0.008039 nan 0.036722
Cash Flow Per Share 0.077516 0.379839 0.326239 0.366216 0.163192 0.163163 0.014244 0.017617 0.016140 0.000758 0.016343 0.007253 0.052162 0.353883 0.015297 0.068363 0.346904 0.346511 0.346243 0.455794 1.000000 0.006983 0.460740 0.439257 0.104689 0.005481 0.001412 0.000488 0.000375 0.054534 0.009383 0.187418 0.650279 0.000581 0.025135 0.003575 0.003779 0.158117 0.158117 0.030964 0.099198 0.009521 0.465102 0.445817 0.125508 0.052539 0.020618 0.010239 0.037357 0.013248 0.089648 0.008271 0.062192 0.010749 0.073991 0.098143 0.037778 0.253610 0.013814 0.022608 0.147717 0.415122 0.009112 0.028555 0.006296 0.009904 0.045022 0.031188 0.225557 0.011126 0.013919 0.042716 0.028332 0.000240 0.058218 0.004107 0.023987 0.006296 0.045022 0.037488 0.246791 0.129250 0.715003 0.199675 0.052804 0.037403 0.292252 0.023591 0.002721 0.163190 0.074250 0.047298 0.006200 0.001358 nan 0.052117
Revenue Per Share (Yuan ¥) 0.004692 0.015932 0.011829 0.014359 0.117045 0.117196 0.044460 0.004931 0.005594 0.118316 0.051607 0.015838 0.019291 0.201679 0.003904 0.021313 0.008235 0.008193 0.008222 0.009690 0.006983 1.000000 0.014822 0.011663 0.000192 0.000427 0.058294 0.058071 0.003503 0.010670 0.000353 0.001965 0.000720 0.000311 0.000880 0.000710 0.029592 0.019679 0.019679 0.128149 0.001181 0.004573 0.014768 0.011463 0.009711 0.035980 0.264346 0.000985 0.011734 0.010462 0.020826 0.275742 0.025973 0.000982 0.020298 0.014145 0.000271 0.018125 0.000538 0.001871 0.026361 0.127459 0.000279 0.037494 0.027977 0.001783 0.006360 0.002441 0.004366 0.000558 0.017885 0.010894 0.016476 0.184916 0.021618 0.037165 0.000311 0.027977 0.006360 0.010574 0.015799 0.156966 0.002226 0.003660 0.006224 0.000881 0.008315 0.001272 0.027256 0.117044 0.001104 0.002132 0.001140 0.000053 nan 0.233203
Operating Profit Per Share (Yuan ¥) 0.142051 0.687201 0.654253 0.659834 0.267944 0.267021 0.022397 0.026314 0.024137 0.001601 0.024516 0.071799 0.068738 0.191974 0.018363 0.197467 0.607623 0.606954 0.606895 0.876769 0.460740 0.014822 1.000000 0.861813 0.002527 0.027941 0.067502 0.067182 0.012131 0.041334 0.022639 0.076453 0.205792 0.001014 0.000720 0.000004 0.013880 0.078056 0.078056 0.043273 0.107135 0.056626 0.998696 0.886157 0.020064 0.282591 0.019500 0.002794 0.046790 0.119366 0.142435 0.009899 0.367024 0.008020 0.266177 0.266637 0.260501 0.236888 0.007192 0.023966 0.003496 0.245701 0.003723 0.037542 0.135996 0.126546 0.055461 0.022299 0.415226 0.015660 0.070742 0.008130 0.001263 0.000435 0.012514 0.005120 0.006732 0.135996 0.055461 0.094285 0.218511 0.118816 0.338206 0.126165 0.145741 0.046381 0.577846 0.032299 0.001169 0.267946 0.183601 0.077102 0.015936 0.006331 nan 0.009316
Per Share Net profit before tax (Yuan ¥) 0.201395 0.750564 0.752578 0.722940 0.247789 0.246004 0.020219 0.034046 0.030621 0.019279 0.030487 0.081428 0.066085 0.177008 0.061431 0.208763 0.726321 0.725956 0.725825 0.955591 0.439257 0.011663 0.861813 1.000000 0.000496 0.029782 0.090223 0.090368 0.024448 0.055298 0.037629 0.120135 0.168160 0.009684 0.006931 0.001111 0.008518 0.158897 0.158897 0.047935 0.142138 0.047234 0.858310 0.962723 0.031613 0.230325 0.020045 0.004424 0.073633 0.133878 0.089929 0.014148 0.325783 0.008783 0.238435 0.219267 0.175784 0.222925 0.006873 0.034862 0.079795 0.222474 0.002642 0.033599 0.106568 0.114848 0.089174 0.047070 0.473736 0.022244 0.156954 0.009443 0.024310 0.001388 0.029827 0.006428 0.006482 0.106568 0.089174 0.110478 0.224392 0.123983 0.308200 0.105591 0.148721 0.104995 0.671748 0.028837 0.008267 0.247791 0.218389 0.107727 0.017885 0.008143 nan 0.028185
Realized Sales Gross Profit Growth Rate 0.000458 0.000591 0.003277 0.002142 0.014172 0.014188 0.000831 0.001246 0.001226 0.000484 0.001207 0.008170 0.011151 0.017070 0.003299 0.001507 0.013744 0.013689 0.013708 0.002178 0.104689 0.000192 0.002527 0.000496 1.000000 0.002192 0.006470 0.006444 0.000747 0.035116 0.000698 0.005843 0.049794 0.000285 0.003220 0.000260 0.000589 0.011461 0.011461 0.001248 0.004898 0.001484 0.002517 0.002674 0.018133 0.096856 0.000630 0.000937 0.007853 0.005697 0.061691 0.000539 0.001962 0.001002 0.012096 0.015756 0.028146 0.005605 0.000494 0.001799 0.021559 0.023343 0.000121 0.001290 0.016519 0.008683 0.004456 0.001783 0.008902 0.000307 0.050982 0.019388 0.003755 0.000023 0.009126 0.000182 0.000301 0.016519 0.004456 0.004258 0.004178 0.002393 0.046844 0.005539 0.002813 0.002142 0.003064 0.002692 0.000764 0.014172 0.001952 0.001687 0.000672 0.000327 nan 0.002302
Operating Profit Growth Rate 0.015168 0.036511 0.042208 0.036144 0.022867 0.022778 0.004952 0.003909 0.002962 0.004200 0.002643 0.013374 0.012166 0.003731 0.001783 0.018784 0.016049 0.016014 0.016022 0.025088 0.005481 0.000427 0.027941 0.029782 0.002192 1.000000 0.639394 0.636793 0.100821 0.015553 0.004534 0.326720 0.000856 0.000089 0.000404 0.006090 0.000051 0.018100 0.018100 0.001654 0.002009 0.002708 0.027680 0.027997 0.005847 0.044088 0.025684 0.001395 0.002095 0.007908 0.033000 0.000853 0.015480 0.000348 0.024150 0.012454 0.018826 0.008048 0.000145 0.002419 0.006721 0.003664 0.000081 0.001564 0.011871 0.003457 0.005973 0.001546 0.021450 0.000806 0.004173 0.011499 0.012411 0.005974 0.014003 0.005175 0.000042 0.011871 0.005973 0.012554 0.008680 0.003389 0.002719 0.020307 0.021073 0.046146 0.041046 0.000063 0.000180 0.022866 0.007570 0.000537 0.001247 0.004576 nan 0.001725
After-tax Net Profit Growth Rate 0.037783 0.115083 0.125384 0.117130 0.054639 0.054470 0.011328 0.035150 0.031223 0.043179 0.016584 0.007176 0.019958 0.019071 0.010106 0.047621 0.056818 0.056679 0.056682 0.086080 0.001412 0.058294 0.067502 0.090223 0.006470 0.639394 1.000000 0.996186 0.113051 0.008039 0.003660 0.223919 0.020630 0.000201 0.000796 0.006938 0.000535 0.030240 0.030240 0.003240 0.012188 0.004558 0.066870 0.088074 0.005673 0.059724 0.019195 0.003642 0.013040 0.005390 0.035121 0.129766 0.046260 0.000548 0.031928 0.028478 0.015090 0.020819 0.000469 0.005475 0.021936 0.015927 0.001996 0.019321 0.000929 0.004411 0.007517 0.005114 0.064121 0.002600 0.031487 0.005805 0.014418 0.000371 0.014097 0.000916 0.000381 0.000929 0.007517 0.018494 0.027411 0.015736 0.014993 0.006925 0.033598 0.050839 0.119596 0.001185 0.002108 0.054639 0.020203 0.011685 0.002030 0.005373 nan 0.001253
Regular Net Profit Growth Rate 0.036820 0.115040 0.125872 0.117042 0.053430 0.053259 0.011227 0.034914 0.030964 0.042951 0.016415 0.009511 0.020703 0.018300 0.010021 0.047701 0.056518 0.056375 0.056378 0.086083 0.000488 0.058071 0.067182 0.090368 0.006444 0.636793 0.996186 1.000000 0.112904 0.008911 0.003649 0.223106 0.019473 0.000186 0.000783 0.006874 0.000524 0.030512 0.030512 0.003390 0.012109 0.004274 0.066552 0.088255 0.005827 0.060223 0.019103 0.002623 0.013363 0.004697 0.035224 0.128905 0.046101 0.000535 0.031764 0.028959 0.014820 0.020039 0.000461 0.005428 0.022080 0.015185 0.001776 0.019272 0.000933 0.004243 0.007521 0.004905 0.064422 0.002599 0.032827 0.005591 0.014146 0.000198 0.013462 0.000756 0.000376 0.000933 0.007521 0.018514 0.026884 0.015741 0.013393 0.007324 0.033395 0.050639 0.119870 0.001166 0.002026 0.053430 0.020273 0.011705 0.002014 0.005329 nan 0.001383
Continuous Net Profit Growth Rate 0.009401 0.025234 0.024887 0.024414 0.009121 0.009117 0.001318 0.003013 0.002565 0.002855 0.001842 0.006644 0.007842 0.003902 0.000499 0.016857 0.043865 0.043767 0.043751 0.023707 0.000375 0.003503 0.012131 0.024448 0.000747 0.100821 0.113051 0.112904 1.000000 0.010175 0.000270 0.036198 0.003750 0.000060 0.000120 0.001145 0.000154 0.025205 0.025205 0.000964 0.007715 0.001580 0.012002 0.025103 0.006369 0.000215 0.002989 0.000717 0.012954 0.003729 0.001916 0.007249 0.005900 0.000199 0.004795 0.003351 0.007937 0.001619 0.000081 0.001146 0.016883 0.003403 0.060758 0.001519 0.003392 0.001327 0.006179 0.000507 0.011965 0.000653 0.013009 0.002073 0.007177 0.000385 0.006509 0.000180 0.000043 0.003392 0.006179 0.008501 0.000208 0.000120 0.001262 0.000834 0.009174 0.002059 0.024257 0.000307 0.002108 0.009122 0.006638 0.007433 0.000014 0.001086 nan 0.014498
Total Asset Growth Rate 0.044431 0.019635 0.026977 0.022104 0.016013 0.016583 0.034465 0.037633 0.037066 0.008224 0.034962 0.014168 0.023189 0.055389 0.018700 0.067886 0.018871 0.018650 0.018389 0.036743 0.054534 0.010670 0.041334 0.055298 0.035116 0.015553 0.008039 0.008911 0.010175 1.000000 0.008688 0.037136 0.063710 0.006779 0.013451 0.007325 0.012469 0.049191 0.049191 0.033926 0.019354 0.001136 0.038978 0.028557 0.045200 0.072393 0.030866 0.035608 0.030277 0.009155 0.084994 0.017423 0.003777 0.010750 0.025894 0.068393 0.077647 0.064079 0.002528 0.006808 0.069315 0.064132 0.010147 0.011164 0.044135 0.013873 0.036283 0.016918 0.099163 0.020221 0.079619 0.000739 0.032340 0.004511 0.064419 0.004524 0.006570 0.044135 0.036283 0.013855 0.104641 0.064552 0.100864 0.048385 0.027703 0.027766 0.080031 0.038909 0.013174 0.016014 0.032565 0.033052 0.005520 0.001723 nan 0.015962
Net Value Growth Rate 0.065329 0.021930 0.063970 0.026127 0.017448 0.017451 0.000207 0.000998 0.000858 0.001505 0.000354 0.008456 0.010300 0.007420 0.013614 0.011380 0.030175 0.030089 0.030099 0.022101 0.009383 0.000353 0.022639 0.037629 0.000698 0.004534 0.003660 0.003649 0.000270 0.008688 1.000000 0.004653 0.013420 0.000166 0.000470 0.000650 0.000360 0.056508 0.056508 0.001914 0.077942 0.001584 0.022705 0.038361 0.012109 0.006532 0.000631 0.000526 0.009009 0.036004 0.063203 0.000234 0.012497 0.000524 0.044672 0.019193 0.025684 0.010481 0.075915 0.000999 0.024605 0.006967 0.000552 0.001316 0.016129 0.063047 0.055279 0.001303 0.047416 0.000709 0.042600 0.008879 0.006129 0.000367 0.002081 0.000039 0.000166 0.016129 0.055279 0.071401 0.008237 0.001937 0.018421 0.013669 0.117590 0.400342 0.072408 0.000679 0.010080 0.017450 0.068054 0.068649 0.000697 0.000446 nan 0.010685
Total Asset Return Growth Rate Ratio 0.016858 0.079906 0.081982 0.079972 0.026545 0.026463 0.003677 0.005004 0.004379 0.001115 0.004203 0.003863 0.029752 0.002779 0.004281 0.009976 0.060968 0.060835 0.060802 0.080140 0.187418 0.001965 0.076453 0.120135 0.005843 0.326720 0.223919 0.223106 0.036198 0.037136 0.004653 1.000000 0.032078 0.000737 0.000135 0.001643 0.000794 0.001100 0.001100 0.002237 0.009720 0.004014 0.052078 0.062054 0.007472 0.041372 0.011291 0.001482 0.013148 0.011274 0.027419 0.003493 0.022437 0.001571 0.026427 0.024787 0.038872 0.013891 0.000358 0.002480 0.016977 0.003354 0.000492 0.001824 0.027073 0.015315 0.001407 0.001410 0.025056 0.000794 0.015412 0.005130 0.009262 0.001643 0.018438 0.000610 0.000206 0.027073 0.001407 0.011811 0.038569 0.019516 0.023001 0.022967 0.008920 0.009823 0.062183 0.006583 0.000310 0.026544 0.019467 0.005198 0.000310 0.001498 nan 0.002454
Cash Reinvestment % 0.051345 0.296158 0.263615 0.292008 0.122676 0.122787 0.014955 0.017801 0.016692 0.000606 0.017400 0.003016 0.042269 0.344660 0.000198 0.065329 0.090651 0.090377 0.090312 0.165224 0.650279 0.000720 0.205792 0.168160 0.049794 0.000856 0.020630 0.019473 0.003750 0.063710 0.013420 0.032078 1.000000 0.004268 0.022360 0.011983 0.014103 0.134276 0.134276 0.047478 0.185446 0.006771 0.203603 0.158675 0.195899 0.055909 0.018598 0.012798 0.021508 0.015146 0.103647 0.003601 0.014826 0.005393 0.056922 0.071882 0.047558 0.167651 0.028415 0.016929 0.138271 0.412683 0.005482 0.020751 0.011324 0.036706 0.167594 0.023165 0.205944 0.004849 0.060663 0.037312 0.022286 0.003031 0.041483 0.006674 0.034956 0.011324 0.167594 0.018924 0.247814 0.136652 0.738276 0.089089 0.052971 0.118872 0.252716 0.041889 0.008558 0.122675 0.162473 0.133686 0.003717 0.005525 nan 0.045546
Current Ratio 0.002211 0.013196 0.014102 0.012975 0.024945 0.024984 0.000833 0.003164 0.002685 0.004344 0.002887 0.007464 0.009092 0.002257 0.001840 0.000010 0.010462 0.010446 0.010429 0.011286 0.000581 0.000311 0.001014 0.009684 0.000285 0.000089 0.000201 0.000186 0.000060 0.006779 0.000166 0.000737 0.004268 1.000000 0.000415 0.000379 0.000318 0.015860 0.015860 0.000118 0.002542 0.004485 0.000993 0.010726 0.007975 0.015341 0.000557 0.000464 0.007943 0.004931 0.009229 0.000206 0.038839 0.000463 0.020116 0.023992 0.029002 0.010622 0.000254 0.000882 0.012141 0.003547 0.000486 0.001161 0.004915 0.005779 0.003582 0.001150 0.015432 0.003411 0.011762 0.002240 0.007291 0.000487 0.003635 0.000101 0.000147 0.004915 0.003582 0.002434 0.006635 0.005370 0.001195 0.002907 0.347630 0.000415 0.014946 0.000599 0.008178 0.024946 0.002489 0.003741 0.000574 0.000150 nan 0.010228
Quick Ratio 0.025058 0.026336 0.018412 0.024232 0.001379 0.001418 0.000323 0.017376 0.015369 0.035784 0.016857 0.017687 0.025702 0.012833 0.005201 0.024875 0.002909 0.002874 0.002013 0.004244 0.025135 0.000880 0.000720 0.006931 0.003220 0.000404 0.000796 0.000783 0.000120 0.013451 0.000470 0.000135 0.022360 0.000415 1.000000 0.003143 0.000898 0.033410 0.033410 0.001013 0.016767 0.001224 0.000816 0.006861 0.037141 0.037770 0.001574 0.016026 0.019058 0.001797 0.020018 0.000583 0.074423 0.001309 0.009233 0.063064 0.027031 0.029790 0.000717 0.151987 0.048153 0.013926 0.008347 0.003283 0.028012 0.003585 0.014777 0.003252 0.011885 0.000314 0.018296 0.014514 0.007069 0.002905 0.022126 0.000025 0.000415 0.028012 0.014777 0.003287 0.008936 0.004089 0.023447 0.005717 0.006420 0.001173 0.017779 0.001694 0.014929 0.001381 0.002374 0.009645 0.000083 0.001867 nan 0.017084
Interest Expense Ratio 0.002681 0.003988 0.005440 0.005187 0.002366 0.002509 0.001156 0.001630 0.001612 0.000465 0.001479 0.024446 0.013572 0.000513 0.004360 0.034656 0.008175 0.008085 0.008056 0.000007 0.003575 0.000710 0.000004 0.001111 0.000260 0.006090 0.006938 0.006874 0.001145 0.007325 0.000650 0.001643 0.011983 0.000379 0.003143 1.000000 0.000797 0.023196 0.023196 0.007224 0.007844 0.000074 0.000028 0.001346 0.005507 0.002619 0.001307 0.001691 0.011133 0.025936 0.006706 0.003175 0.008881 0.002950 0.014455 0.016619 0.009594 0.017431 0.001858 0.010633 0.006254 0.001680 0.004150 0.001318 0.022008 0.002860 0.004542 0.006971 0.000065 0.000430 0.013466 0.030494 0.007865 0.000240 0.002048 0.000038 0.013903 0.022008 0.004542 0.008543 0.011678 0.004043 0.008880 0.011266 0.014302 0.001530 0.004969 0.000586 0.000076 0.002365 0.003604 0.006926 0.016829 0.034321 nan 0.012626
Total debt/Total net worth 0.012314 0.022208 0.010323 0.021161 0.022360 0.022354 0.001507 0.001964 0.001379 0.000284 0.000075 0.016164 0.019292 0.349639 0.003984 0.010497 0.008546 0.008547 0.008514 0.011383 0.003779 0.029592 0.013880 0.008518 0.000589 0.000051 0.000535 0.000524 0.000154 0.012469 0.000360 0.000794 0.014103 0.000318 0.000898 0.000797 1.000000 0.054049 0.054049 0.030256 0.008080 0.001269 0.013835 0.008575 0.008896 0.027847 0.008627 0.001005 0.013441 0.016062 0.018407 0.000446 0.025393 0.001002 0.051384 0.002856 0.016898 0.019683 0.000549 0.001910 0.045142 0.265843 0.000574 0.002515 0.006780 0.008081 0.009967 0.002491 0.009129 0.000725 0.015686 0.011118 0.016815 0.015300 0.016561 0.004189 0.000318 0.006780 0.009967 0.006328 0.022285 0.149285 0.023123 0.006953 0.025966 0.000899 0.008056 0.001298 0.002556 0.022363 0.000700 0.010045 0.001262 0.000431 nan 0.338898
Debt ratio % 0.250161 0.261427 0.259972 0.264734 0.245460 0.245606 0.010397 0.003906 0.006174 0.033214 0.001192 0.143833 0.045162 0.285010 0.059911 0.009724 0.249146 0.249925 0.249463 0.177429 0.158117 0.019679 0.078056 0.158897 0.011461 0.018100 0.030240 0.030512 0.025205 0.049191 0.056508 0.001100 0.134276 0.015860 0.033410 0.023196 0.054049 1.000000 1.000000 0.010523 0.329109 0.058862 0.077250 0.164110 0.417868 0.237458 0.029924 0.006062 0.043629 0.010258 0.436160 0.037412 0.007547 0.010321 0.528797 0.084232 0.109964 0.357605 0.039455 0.082804 0.842583 0.333429 0.002064 0.026934 0.082322 0.105008 0.343692 0.006936 0.235423 0.027995 0.037513 0.124880 0.203370 0.031715 0.138934 0.018201 0.023418 0.082322 0.343692 0.244974 0.066502 0.076983 0.268159 0.003178 0.428180 0.203155 0.281422 0.041055 0.050218 0.245461 0.123986 0.349250 0.017982 0.012571 nan 0.625879
Net worth/Assets 0.250161 0.261427 0.259972 0.264734 0.245460 0.245606 0.010397 0.003906 0.006174 0.033214 0.001192 0.143833 0.045162 0.285010 0.059911 0.009724 0.249146 0.249925 0.249463 0.177429 0.158117 0.019679 0.078056 0.158897 0.011461 0.018100 0.030240 0.030512 0.025205 0.049191 0.056508 0.001100 0.134276 0.015860 0.033410 0.023196 0.054049 1.000000 1.000000 0.010523 0.329109 0.058862 0.077250 0.164110 0.417868 0.237458 0.029924 0.006062 0.043629 0.010258 0.436160 0.037412 0.007547 0.010321 0.528797 0.084232 0.109964 0.357605 0.039455 0.082804 0.842583 0.333429 0.002064 0.026934 0.082322 0.105008 0.343692 0.006936 0.235423 0.027995 0.037513 0.124880 0.203370 0.031715 0.138934 0.018201 0.023418 0.082322 0.343692 0.244974 0.066502 0.076983 0.268159 0.003178 0.428180 0.203155 0.281422 0.041055 0.050218 0.245461 0.123986 0.349250 0.017982 0.012571 nan 0.625879
Long-term fund suitability ratio (A) 0.016920 0.002967 0.020707 0.003869 0.006020 0.006189 0.000833 0.001907 0.001227 0.001809 0.000187 0.008990 0.047014 0.038308 0.002245 0.012952 0.052254 0.052245 0.052137 0.052178 0.030964 0.128149 0.043273 0.047935 0.001248 0.001654 0.003240 0.003390 0.000964 0.033926 0.001914 0.002237 0.047478 0.000118 0.001013 0.007224 0.030256 0.010523 0.010523 1.000000 0.000684 0.001385 0.043298 0.049832 0.048349 0.045883 0.007167 0.023772 0.040272 0.048631 0.023839 0.002326 0.178720 0.342925 0.077891 0.040821 0.104518 0.052845 0.001740 0.013864 0.036649 0.026117 0.000936 0.000367 0.074428 0.031667 0.013201 0.003992 0.010451 0.052586 0.018443 0.015572 0.001652 0.031637 0.014006 0.008499 0.393705 0.074428 0.013201 0.021716 0.027432 0.052517 0.031758 0.011582 0.021608 0.005833 0.016566 0.003589 0.003389 0.006020 0.008679 0.001791 0.004319 0.000342 nan 0.104344
Borrowing dependency 0.176543 0.161671 0.161868 0.158618 0.085733 0.085598 0.001092 0.004654 0.004395 0.012037 0.002887 0.023977 0.045529 0.078763 0.001729 0.053177 0.123991 0.125321 0.125188 0.144138 0.099198 0.001181 0.107135 0.142138 0.004898 0.002009 0.012188 0.012109 0.007715 0.019354 0.077942 0.009720 0.185446 0.002542 0.016767 0.007844 0.008080 0.329109 0.329109 0.000684 1.000000 0.451737 0.104721 0.134924 0.700139 0.006443 0.034113 0.003798 0.010203 0.025232 0.177337 0.029949 0.009385 0.004641 0.192554 0.095568 0.017770 0.126419 0.067930 0.041094 0.229825 0.093248 0.003151 0.007702 0.075594 0.535714 0.892772 0.007554 0.117913 0.007016 0.022642 0.028854 0.063473 0.004406 0.004608 0.000476 0.015588 0.075594 0.892772 0.806889 0.026453 0.022185 0.117624 0.193543 0.124908 0.259702 0.177781 0.011083 0.004183 0.085732 0.806478 0.955857 0.007260 0.001776 nan 0.146012
Contingent liabilities/Net worth 0.070455 0.035729 0.036183 0.034177 0.022258 0.022239 0.000247 0.002222 0.002117 0.003873 0.002166 0.010618 0.012449 0.009771 0.002792 0.012504 0.028406 0.028915 0.028923 0.053962 0.009521 0.004573 0.056626 0.047234 0.001484 0.002708 0.004558 0.004274 0.001580 0.001136 0.001584 0.004014 0.006771 0.004485 0.001224 0.000074 0.001269 0.058862 0.058862 0.001385 0.451737 1.000000 0.056837 0.049424 0.077200 0.020659 0.048691 0.000373 0.014864 0.006061 0.092726 0.000841 0.024133 0.005206 0.065879 0.015814 0.028117 0.019245 0.000942 0.011087 0.049283 0.011000 0.001142 0.000778 0.004683 0.767778 0.622905 0.001512 0.021729 0.000202 0.007404 0.001941 0.000955 0.001101 0.000351 0.000541 0.000599 0.004683 0.622905 0.462858 0.013533 0.004790 0.011931 0.295827 0.084231 0.005567 0.037822 0.002446 0.001531 0.022258 0.352618 0.621808 0.001061 0.000620 nan 0.018260
Operating profit/Paid-in capital 0.141111 0.685028 0.651581 0.657274 0.267411 0.266483 0.022160 0.026091 0.023942 0.001473 0.024295 0.071972 0.067143 0.191723 0.016198 0.197419 0.603024 0.602367 0.602277 0.873641 0.465102 0.014768 0.998696 0.858310 0.002517 0.027680 0.066870 0.066552 0.012002 0.038978 0.022705 0.052078 0.203603 0.000993 0.000816 0.000028 0.013835 0.077250 0.077250 0.043298 0.104721 0.056837 1.000000 0.887370 0.020421 0.281435 0.019353 0.003185 0.047084 0.118531 0.142561 0.009861 0.366215 0.008027 0.264617 0.266090 0.259411 0.235767 0.007147 0.023862 0.002894 0.245298 0.003727 0.038217 0.135385 0.124996 0.053169 0.022794 0.413869 0.015657 0.069488 0.008815 0.000433 0.000332 0.012464 0.005206 0.006723 0.135385 0.053169 0.094417 0.216378 0.117421 0.337872 0.132425 0.144231 0.046447 0.575833 0.032403 0.000975 0.267412 0.181150 0.075374 0.016052 0.006258 nan 0.009528
Net profit before tax/Paid-in capital 0.207857 0.753339 0.758234 0.726003 0.248104 0.246256 0.020015 0.033900 0.030568 0.019483 0.030334 0.082923 0.069545 0.186619 0.054972 0.209898 0.706646 0.705800 0.705621 0.959461 0.445817 0.011463 0.886157 0.962723 0.002674 0.027997 0.088074 0.088255 0.025103 0.028557 0.038361 0.062054 0.158675 0.010726 0.006861 0.001346 0.008575 0.164110 0.164110 0.049832 0.134924 0.049424 0.887370 1.000000 0.030408 0.221105 0.017993 0.005425 0.073815 0.134096 0.078373 0.013189 0.333660 0.008584 0.245821 0.222639 0.178409 0.228365 0.007288 0.035314 0.086021 0.233192 0.002615 0.036318 0.102487 0.115605 0.083819 0.022193 0.483355 0.023754 0.174815 0.008198 0.023197 0.001825 0.031204 0.006864 0.006716 0.102487 0.083819 0.111974 0.218673 0.122515 0.318991 0.130916 0.153719 0.109981 0.683623 0.029308 0.007939 0.248106 0.215690 0.104149 0.018358 0.008172 nan 0.031292
Inventory and accounts receivable/Net value 0.075278 0.109888 0.078585 0.109501 0.086720 0.086762 0.011993 0.009042 0.008993 0.011027 0.008243 0.079747 0.047222 0.125484 0.035486 0.027022 0.089396 0.089992 0.089777 0.037986 0.125508 0.009711 0.020064 0.031613 0.018133 0.005847 0.005673 0.005827 0.006369 0.045200 0.012109 0.007472 0.195899 0.007975 0.037141 0.005507 0.008896 0.417868 0.417868 0.048349 0.700139 0.077200 0.020421 0.030408 1.000000 0.243057 0.042552 0.010940 0.002019 0.124019 0.453424 0.005265 0.118059 0.014592 0.002128 0.100201 0.337547 0.135674 0.015249 0.050400 0.452321 0.130778 0.008687 0.012449 0.149187 0.040188 0.661805 0.017873 0.064067 0.010417 0.037055 0.041233 0.112787 0.000594 0.049393 0.003459 0.035281 0.149187 0.661805 0.486097 0.025453 0.023877 0.168028 0.159920 0.027166 0.204500 0.094770 0.027074 0.021709 0.086720 0.422238 0.670373 0.006647 0.006764 nan 0.205183
Total Asset Turnover 0.067915 0.210622 0.223528 0.194810 0.099661 0.100141 0.029456 0.029667 0.029504 0.012057 0.027711 0.195063 0.013498 0.057978 0.008551 0.193636 0.082026 0.082674 0.082434 0.214710 0.052539 0.035980 0.282591 0.230325 0.096856 0.044088 0.059724 0.060223 0.000215 0.072393 0.006532 0.041372 0.055909 0.015341 0.037770 0.002619 0.027847 0.237458 0.237458 0.045883 0.006443 0.020659 0.281435 0.221105 0.243057 1.000000 0.060229 0.004841 0.152298 0.299431 0.757414 0.023836 0.061330 0.035084 0.211156 0.468956 0.497405 0.108064 0.018390 0.047976 0.384428 0.030660 0.021439 0.013638 0.321993 0.100832 0.124257 0.018985 0.124320 0.014172 0.171386 0.353826 0.252558 0.006442 0.060722 0.007064 0.016059 0.321993 0.124257 0.020655 0.093131 0.037509 0.018741 0.052024 0.087297 0.012599 0.188774 0.041944 0.007536 0.099661 0.041242 0.084243 0.020769 0.019358 nan 0.198485
Accounts Receivable Turnover 0.004754 0.033947 0.031262 0.033768 0.082342 0.082479 0.023171 0.089576 0.079920 0.236897 0.100604 0.028331 0.034508 0.005904 0.006983 0.019968 0.018647 0.019675 0.019724 0.019997 0.020618 0.264346 0.019500 0.020045 0.000630 0.025684 0.019195 0.019103 0.002989 0.030866 0.000631 0.011291 0.018598 0.000557 0.001574 0.001307 0.008627 0.029924 0.029924 0.007167 0.034113 0.048691 0.019353 0.017993 0.042552 0.060229 1.000000 0.001762 0.013201 0.001709 0.032071 0.032398 0.020483 0.001757 0.040971 0.009706 0.006896 0.017054 0.000962 0.036912 0.044754 0.005032 0.235196 0.048829 0.012795 0.015012 0.033322 0.001859 0.016678 0.000684 0.018303 0.019486 0.029472 0.074727 0.022508 0.052668 0.000557 0.012795 0.033322 0.008364 0.012486 0.022651 0.026659 0.005331 0.021219 0.001575 0.025062 0.113731 0.004030 0.082343 0.006587 0.023753 0.002111 0.000497 nan 0.010629
Average Collection Days 0.006556 0.007019 0.009041 0.009921 0.022530 0.022596 0.001001 0.002198 0.002032 0.001987 0.002168 0.007935 0.028471 0.006638 0.016496 0.000443 0.002448 0.002411 0.002455 0.007838 0.010239 0.000985 0.002794 0.004424 0.000937 0.001395 0.003642 0.002623 0.000717 0.035608 0.000526 0.001482 0.012798 0.000464 0.016026 0.001691 0.001005 0.006062 0.006062 0.023772 0.003798 0.000373 0.003185 0.005425 0.010940 0.004841 0.001762 1.000000 0.007906 0.013746 0.005157 0.000653 0.088379 0.000539 0.027486 0.051592 0.011503 0.017105 0.000803 0.018409 0.020865 0.011150 0.009554 0.003676 0.024454 0.005380 0.007744 0.003641 0.010700 0.459146 0.040179 0.021400 0.003969 0.000392 0.014979 0.000073 0.006008 0.024454 0.007744 0.007994 0.002480 0.003105 0.004308 0.000835 0.068197 0.001314 0.011002 0.001897 0.004210 0.022529 0.004093 0.003221 0.001342 0.000892 nan 0.002744
Inventory Turnover Rate (times) 0.001376 0.062660 0.054496 0.053605 0.047665 0.047079 0.009576 0.005560 0.005957 0.012145 0.006203 0.129214 0.001366 0.020141 0.007713 0.050769 0.080684 0.080248 0.080054 0.071460 0.037357 0.011734 0.046790 0.073633 0.007853 0.002095 0.013040 0.013363 0.012954 0.030277 0.009009 0.013148 0.021508 0.007943 0.019058 0.011133 0.013441 0.043629 0.043629 0.040272 0.010203 0.014864 0.047084 0.073815 0.002019 0.152298 0.013201 0.007906 1.000000 0.026388 0.115353 0.000193 0.064956 0.025242 0.068369 0.096696 0.023021 0.012603 0.011180 0.021335 0.059348 0.014231 0.000724 0.051987 0.014910 0.016940 0.018456 0.009397 0.048772 0.011172 0.064959 0.177299 0.014567 0.004528 0.017862 0.000165 0.007658 0.014910 0.018456 0.016432 0.015477 0.000805 0.033695 0.016848 0.054892 0.011811 0.052668 0.009172 0.010280 0.047666 0.026296 0.008359 0.001189 0.011491 nan 0.006726
Fixed Assets Turnover Frequency 0.072818 0.065919 0.136964 0.061046 0.001239 0.001289 0.005232 0.003581 0.004241 0.005540 0.003439 0.055160 0.016872 0.052978 0.036213 0.135210 0.080971 0.081773 0.081749 0.129457 0.013248 0.010462 0.119366 0.133878 0.005697 0.007908 0.005390 0.004697 0.003729 0.009155 0.036004 0.011274 0.015146 0.004931 0.001797 0.025936 0.016062 0.010258 0.010258 0.048631 0.025232 0.006061 0.118531 0.134096 0.124019 0.299431 0.001709 0.013746 0.026388 1.000000 0.183710 0.006700 0.081441 0.015561 0.270936 0.322658 0.386115 0.117399 0.007697 0.017976 0.157522 0.024049 0.005493 0.011197 0.285401 0.097150 0.043548 0.026209 0.100233 0.006679 0.117786 0.042103 0.043957 0.000966 0.039828 0.001644 0.004931 0.285401 0.043548 0.053104 0.052169 0.025605 0.020639 0.026669 0.142589 0.004789 0.122505 0.013477 0.007192 0.001240 0.013604 0.015134 0.018565 0.000537 nan 0.044538
Net Worth Turnover Rate (times) 0.021089 0.022896 0.036925 0.012763 0.136157 0.136335 0.016064 0.015513 0.015736 0.007916 0.014559 0.165135 0.025440 0.096663 0.046759 0.115270 0.032829 0.032440 0.032455 0.066033 0.089648 0.020826 0.142435 0.089929 0.061691 0.033000 0.035121 0.035224 0.001916 0.084994 0.063203 0.027419 0.103647 0.009229 0.020018 0.006706 0.018407 0.436160 0.436160 0.023839 0.177337 0.092726 0.142561 0.078373 0.453424 0.757414 0.032071 0.005157 0.115353 0.183710 1.000000 0.013763 0.011007 0.023205 0.016691 0.299791 0.358480 0.011920 0.105531 0.017293 0.499370 0.090999 0.017728 0.008378 0.194031 0.105820 0.267816 0.014774 0.001481 0.010976 0.114822 0.271284 0.209559 0.004805 0.041663 0.004045 0.008856 0.194031 0.267816 0.223240 0.044739 0.001048 0.057832 0.084083 0.126417 0.172578 0.013776 0.059178 0.019169 0.136158 0.034699 0.273553 0.009233 0.017437 nan 0.226456
Revenue per person 0.039718 0.014834 0.014888 0.014545 0.019022 0.019060 0.027450 0.144956 0.146671 0.225032 0.064693 0.010492 0.012780 0.009018 0.002586 0.014119 0.017799 0.017740 0.017757 0.011412 0.008271 0.275742 0.009899 0.014148 0.000539 0.000853 0.129766 0.128905 0.007249 0.017423 0.000234 0.003493 0.003601 0.000206 0.000583 0.003175 0.000446 0.037412 0.037412 0.002326 0.029949 0.000841 0.009861 0.013189 0.005265 0.023836 0.032398 0.000653 0.000193 0.006700 0.013763 1.000000 0.007845 0.000651 0.030332 0.029715 0.025248 0.014162 0.000356 0.001240 0.006309 0.006774 0.001109 0.086595 0.027657 0.019620 0.013610 0.001617 0.016047 0.000969 0.012086 0.007217 0.010915 0.039606 0.014322 0.019829 0.000206 0.027657 0.013610 0.031914 0.004929 0.001922 0.009930 0.004661 0.027678 0.000583 0.014309 0.000843 0.033598 0.019021 0.006549 0.022624 0.001084 0.002667 nan 0.011295
Operating profit per person 0.092842 0.301996 0.324942 0.304522 0.224976 0.224458 0.018248 0.020271 0.018334 0.003658 0.018580 0.126869 0.043509 0.131304 0.004588 0.067826 0.261330 0.261381 0.261477 0.351589 0.062192 0.025973 0.367024 0.325783 0.001962 0.015480 0.046260 0.046101 0.005900 0.003777 0.012497 0.022437 0.014826 0.038839 0.074423 0.008881 0.025393 0.007547 0.007547 0.178720 0.009385 0.024133 0.366215 0.333660 0.118059 0.061330 0.020483 0.088379 0.064956 0.081441 0.011007 0.007845 1.000000 0.000372 0.159706 0.000507 0.217071 0.048863 0.034704 0.061964 0.078832 0.118073 0.013443 0.005857 0.125118 0.086564 0.010680 0.017067 0.233582 0.124769 0.146846 0.006812 0.047514 0.006489 0.011304 0.000762 0.004646 0.125118 0.010680 0.051603 0.075271 0.053627 0.091023 0.018552 0.050780 0.030241 0.306356 0.017804 0.009823 0.224980 0.096235 0.011415 0.006094 0.003220 nan 0.030261
Allocation rate per person 0.002829 0.012543 0.006035 0.012770 0.006953 0.007262 0.000726 0.000685 0.000702 0.000390 0.000652 0.009231 0.028073 0.001554 0.024254 0.005542 0.009322 0.009268 0.009310 0.009403 0.010749 0.000982 0.008020 0.008783 0.001002 0.000348 0.000548 0.000535 0.000199 0.010750 0.000524 0.001571 0.005393 0.000463 0.001309 0.002950 0.001002 0.010321 0.010321 0.342925 0.004641 0.005206 0.008027 0.008584 0.014592 0.035084 0.001757 0.000539 0.025242 0.015561 0.023205 0.000651 0.000372 1.000000 0.013102 0.035254 0.032207 0.001549 0.000800 0.004314 0.025642 0.000400 0.000525 0.003665 0.023199 0.008668 0.006436 0.001754 0.001538 0.000804 0.010872 0.013957 0.015921 0.000003 0.012579 0.000169 0.142653 0.023199 0.006436 0.008978 0.002807 0.000084 0.008625 0.000878 0.007121 0.001310 0.002153 0.001892 0.000702 0.006952 0.000679 0.002286 0.001859 0.000957 nan 0.000081
Working Capital to Total Assets 0.193083 0.259680 0.303532 0.260151 0.246304 0.246221 0.025599 0.036347 0.040405 0.010800 0.035148 0.076724 0.011881 0.161520 0.045141 0.056103 0.198620 0.199598 0.199475 0.253188 0.073991 0.020298 0.266177 0.238435 0.012096 0.024150 0.031928 0.031764 0.004795 0.025894 0.044672 0.026427 0.056922 0.020116 0.009233 0.014455 0.051384 0.528797 0.528797 0.077891 0.192554 0.065879 0.264617 0.245821 0.002128 0.211156 0.040971 0.027486 0.068369 0.270936 0.016691 0.030332 0.159706 0.013102 1.000000 0.648464 0.714868 0.585770 0.033669 0.056213 0.364453 0.210938 0.012443 0.045342 0.251502 0.336555 0.161580 0.004957 0.217189 0.013297 0.108738 0.184527 0.208682 0.044792 0.140998 0.036276 0.001406 0.251502 0.161580 0.139294 0.233958 0.176265 0.131677 0.101122 0.625560 0.149085 0.297217 0.008452 0.038209 0.246308 0.109766 0.177645 0.036716 0.008912 nan 0.369149
Quick Assets/Total Assets 0.086382 0.181993 0.202017 0.166311 0.152850 0.152805 0.026100 0.030819 0.031453 0.001557 0.028547 0.004686 0.064811 0.031135 0.025246 0.119155 0.115516 0.115606 0.115310 0.215097 0.098143 0.014145 0.266637 0.219267 0.015756 0.012454 0.028478 0.028959 0.003351 0.068393 0.019193 0.024787 0.071882 0.023992 0.063064 0.016619 0.002856 0.084232 0.084232 0.040821 0.095568 0.015814 0.266090 0.222639 0.100201 0.468956 0.009706 0.051592 0.096696 0.322658 0.299791 0.029715 0.000507 0.035254 0.648464 1.000000 0.755453 0.590891 0.039133 0.074537 0.152378 0.098322 0.032204 0.036223 0.462973 0.222932 0.024629 0.032043 0.094297 0.023305 0.242834 0.082370 0.217281 0.011016 0.106712 0.014316 0.021731 0.462973 0.024629 0.115150 0.254929 0.161456 0.084853 0.148667 0.339550 0.000663 0.176086 0.028238 0.028876 0.152850 0.048011 0.024486 0.030419 0.000886 nan 0.095978
Current Assets/Total Assets 0.044823 0.098820 0.157005 0.094083 0.094782 0.094838 0.033821 0.037156 0.037836 0.007613 0.034865 0.025720 0.022853 0.049246 0.013544 0.084386 0.047254 0.047816 0.047968 0.176931 0.037778 0.000271 0.260501 0.175784 0.028146 0.018826 0.015090 0.014820 0.007937 0.077647 0.025684 0.038872 0.047558 0.029002 0.027031 0.009594 0.016898 0.109964 0.109964 0.104518 0.017770 0.028117 0.259411 0.178409 0.337547 0.497405 0.006896 0.011503 0.023021 0.386115 0.358480 0.025248 0.217071 0.032207 0.714868 0.755453 1.000000 0.416425 0.008243 0.003209 0.390629 0.002994 0.011064 0.028397 0.565640 0.289162 0.105331 0.029544 0.065605 0.029451 0.194399 0.111142 0.084330 0.017572 0.044491 0.017334 0.025904 0.565640 0.105331 0.103652 0.202907 0.133968 0.059645 0.120343 0.354198 0.008918 0.133053 0.033760 0.002520 0.094785 0.038880 0.039449 0.031049 0.007281 nan 0.015311
Cash/Total Assets 0.100130 0.235314 0.217918 0.227144 0.241946 0.242353 0.010465 0.017136 0.017118 0.009002 0.016445 0.110605 0.013524 0.228027 0.014468 0.011758 0.185621 0.185631 0.185300 0.240956 0.253610 0.018125 0.236888 0.222925 0.005605 0.008048 0.020819 0.020039 0.001619 0.064079 0.010481 0.013891 0.167651 0.010622 0.029790 0.017431 0.019683 0.357605 0.357605 0.052845 0.126419 0.019245 0.235767 0.228365 0.135674 0.108064 0.017054 0.017105 0.012603 0.117399 0.011920 0.014162 0.048863 0.001549 0.585770 0.590891 0.416425 1.000000 0.016072 0.063908 0.216580 0.300463 0.019091 0.026710 0.243615 0.144209 0.079910 0.002666 0.096487 0.006558 0.184944 0.125317 0.234160 0.014822 0.287559 0.013979 0.010716 0.243615 0.079910 0.095230 0.480174 0.353270 0.281832 0.277303 0.286691 0.012224 0.195652 0.019727 0.026421 0.241945 0.045075 0.097849 0.024289 0.007205 nan 0.299732
Quick Assets/Current Liability 0.003823 0.010530 0.009612 0.010014 0.003206 0.003186 0.000365 0.000296 0.000343 0.000294 0.000300 0.012904 0.015717 0.008364 0.055143 0.009854 0.014827 0.014773 0.014794 0.006744 0.013814 0.000538 0.007192 0.006873 0.000494 0.000145 0.000469 0.000461 0.000081 0.002528 0.075915 0.000358 0.028415 0.000254 0.000717 0.001858 0.000549 0.039455 0.039455 0.001740 0.067930 0.000942 0.007147 0.007288 0.015249 0.018390 0.000962 0.000803 0.011180 0.007697 0.105531 0.000356 0.034704 0.000800 0.033669 0.039133 0.008243 0.016072 1.000000 0.001525 0.055302 0.009208 0.001624 0.002008 0.024001 0.128380 0.077315 0.004567 0.015316 0.000274 0.012822 0.003691 0.001284 0.000310 0.001503 0.000109 0.000254 0.024001 0.077315 0.005047 0.001020 0.001393 0.018524 0.006788 0.202883 0.187510 0.008154 0.001036 0.039538 0.003205 0.021419 0.061051 0.000876 0.000209 nan 0.012449
Cash/Current Liability 0.077921 0.046009 0.037468 0.041296 0.030901 0.031045 0.000301 0.001404 0.000827 0.003560 0.001112 0.024258 0.042133 0.023394 0.011063 0.035265 0.033078 0.032756 0.031013 0.034404 0.022608 0.001871 0.023966 0.034862 0.001799 0.002419 0.005475 0.005428 0.001146 0.006808 0.000999 0.002480 0.016929 0.000882 0.151987 0.010633 0.001910 0.082804 0.082804 0.013864 0.041094 0.011087 0.023862 0.035314 0.050400 0.047976 0.036912 0.018409 0.021335 0.017976 0.017293 0.001240 0.061964 0.004314 0.056213 0.074537 0.003209 0.063908 0.001525 1.000000 0.078276 0.024505 0.002423 0.006982 0.008926 0.015199 0.032776 0.001828 0.025724 0.001625 0.033007 0.002886 0.026529 0.000070 0.044147 0.000009 0.048718 0.008926 0.032776 0.012988 0.020220 0.008704 0.029893 0.014241 0.044378 0.002496 0.034389 0.003604 0.006208 0.030900 0.008188 0.029935 0.001636 0.003422 nan 0.038004
Current Liability to Assets 0.194494 0.210256 0.190501 0.217186 0.198027 0.197842 0.011340 0.001632 0.002805 0.024357 0.000159 0.135256 0.046075 0.278218 0.041390 0.038520 0.198546 0.199086 0.198721 0.097689 0.147717 0.026361 0.003496 0.079795 0.021559 0.006721 0.021936 0.022080 0.016883 0.069315 0.024605 0.016977 0.138271 0.012141 0.048153 0.006254 0.045142 0.842583 0.842583 0.036649 0.229825 0.049283 0.002894 0.086021 0.452321 0.384428 0.044754 0.020865 0.059348 0.157522 0.499370 0.006309 0.078832 0.025642 0.364453 0.152378 0.390629 0.216580 0.055302 0.078276 1.000000 0.273704 0.001647 0.021875 0.422185 0.057977 0.352986 0.032819 0.198553 0.021716 0.115737 0.094912 0.162418 0.035566 0.126370 0.024671 0.032646 0.422185 0.352986 0.045339 0.037780 0.053637 0.252778 0.027142 0.351832 0.184388 0.214085 0.056086 0.053657 0.198028 0.092725 0.286398 0.006987 0.021428 nan 0.506360
Operating Funds to Liability 0.077082 0.388151 0.351107 0.387893 0.246834 0.246781 0.020308 0.022855 0.020877 0.003476 0.024520 0.042396 0.035521 0.880562 0.015720 0.051844 0.195965 0.195903 0.195621 0.248241 0.415122 0.127459 0.245701 0.222474 0.023343 0.003664 0.015927 0.015185 0.003403 0.064132 0.006967 0.003354 0.412683 0.003547 0.013926 0.001680 0.265843 0.333429 0.333429 0.026117 0.093248 0.011000 0.245298 0.233192 0.130778 0.030660 0.005032 0.011150 0.014231 0.024049 0.090999 0.006774 0.118073 0.000400 0.210938 0.098322 0.002994 0.300463 0.009208 0.024505 0.273704 1.000000 0.008492 0.028831 0.062602 0.033619 0.091683 0.042355 0.264403 0.036077 0.072308 0.022532 0.077777 0.030672 0.101758 0.013312 0.008369 0.062602 0.091683 0.058352 0.273598 0.382088 0.702937 0.119707 0.139718 0.016716 0.341188 0.066037 0.010303 0.246833 0.069212 0.091214 0.008961 0.001268 nan 0.398769
Inventory/Working Capital 0.001906 0.004447 0.000004 0.001616 0.035025 0.035085 0.001026 0.010231 0.009501 0.023107 0.011909 0.008018 0.015522 0.006801 0.002820 0.008833 0.005685 0.005589 0.005580 0.000283 0.009112 0.000279 0.003723 0.002642 0.000121 0.000081 0.001996 0.001776 0.060758 0.010147 0.000552 0.000492 0.005482 0.000486 0.008347 0.004150 0.000574 0.002064 0.002064 0.000936 0.003151 0.001142 0.003727 0.002615 0.008687 0.021439 0.235196 0.009554 0.000724 0.005493 0.017728 0.001109 0.013443 0.000525 0.012443 0.032204 0.011064 0.019091 0.001624 0.002423 0.001647 0.008492 1.000000 0.003883 0.010507 0.000220 0.003157 0.000724 0.001636 0.000249 0.018227 0.008716 0.001269 0.000116 0.002589 0.000085 0.001658 0.010507 0.003157 0.001960 0.005108 0.001131 0.010441 0.008403 0.002946 0.002435 0.000992 0.000455 0.001040 0.035024 0.000176 0.001788 0.002294 0.023608 nan 0.005719
Inventory/Current Liability 0.000822 0.013330 0.004864 0.007302 0.035218 0.035478 0.001748 0.017489 0.015766 0.030963 0.005399 0.011448 0.023691 0.019862 0.011240 0.017662 0.015542 0.015540 0.015649 0.031821 0.028555 0.037494 0.037542 0.033599 0.001290 0.001564 0.019321 0.019272 0.001519 0.011164 0.001316 0.001824 0.020751 0.001161 0.003283 0.001318 0.002515 0.026934 0.026934 0.000367 0.007702 0.000778 0.038217 0.036318 0.012449 0.013638 0.048829 0.003676 0.051987 0.011197 0.008378 0.086595 0.005857 0.003665 0.045342 0.036223 0.028397 0.026710 0.002008 0.006982 0.021875 0.028831 0.003883 1.000000 0.000652 0.015531 0.005083 0.001069 0.016548 0.001274 0.006701 0.012131 0.029148 0.003880 0.017556 0.002274 0.001161 0.000652 0.005083 0.009926 0.023234 0.020917 0.026108 0.016766 0.125024 0.023507 0.003639 0.002637 0.002553 0.035217 0.004009 0.007637 0.000344 0.005260 nan 0.004448
Current Liabilities/Liability 0.020809 0.052783 0.080401 0.046694 0.063547 0.064657 0.020520 0.019009 0.013732 0.011739 0.014602 0.013653 0.007120 0.068729 0.025009 0.064182 0.044689 0.044624 0.044538 0.107310 0.006296 0.027977 0.135996 0.106568 0.016519 0.011871 0.000929 0.000933 0.003392 0.044135 0.016129 0.027073 0.011324 0.004915 0.028012 0.022008 0.006780 0.082322 0.082322 0.074428 0.075594 0.004683 0.135385 0.102487 0.149187 0.321993 0.012795 0.024454 0.014910 0.285401 0.194031 0.027657 0.125118 0.023199 0.251502 0.462973 0.565640 0.243615 0.024001 0.008926 0.422185 0.062602 0.010507 0.000652 1.000000 0.071259 0.086931 0.072485 0.027070 0.013303 0.192153 0.038198 0.039084 0.026895 0.006543 0.022191 0.013908 1.000000 0.086931 0.234454 0.054809 0.046433 0.003067 0.041194 0.039736 0.007157 0.066277 0.034229 0.016296 0.063546 0.017171 0.017927 0.017911 0.009450 nan 0.098930
Working Capital/Equity 0.147221 0.103819 0.120403 0.101962 0.067970 0.067921 0.010600 0.014933 0.016788 0.004236 0.014826 0.007324 0.005879 0.022609 0.068273 0.041038 0.070112 0.070331 0.070383 0.121854 0.009904 0.001783 0.126546 0.114848 0.008683 0.003457 0.004411 0.004243 0.001327 0.013873 0.063047 0.015315 0.036706 0.005779 0.003585 0.002860 0.008081 0.105008 0.105008 0.031667 0.535714 0.767778 0.124996 0.115605 0.040188 0.100832 0.015012 0.005380 0.016940 0.097150 0.105820 0.019620 0.086564 0.008668 0.336555 0.222932 0.289162 0.144209 0.128380 0.015199 0.057977 0.033619 0.000220 0.015531 0.071259 1.000000 0.692675 0.001969 0.076849 0.003233 0.008948 0.048945 0.049088 0.016589 0.020252 0.014279 0.007193 0.071259 0.692675 0.353136 0.065755 0.042518 0.016175 0.256678 0.176390 0.160307 0.123817 0.003121 0.009143 0.067970 0.585971 0.650474 0.011134 0.002063 nan 0.058512
Current Liabilities/Equity 0.153828 0.142734 0.133816 0.142879 0.080422 0.080350 0.001860 0.002202 0.003196 0.008969 0.002689 0.035899 0.037452 0.087567 0.018880 0.015970 0.102098 0.102539 0.102431 0.094966 0.045022 0.006360 0.055461 0.089174 0.004456 0.005973 0.007517 0.007521 0.006179 0.036283 0.055279 0.001407 0.167594 0.003582 0.014777 0.004542 0.009967 0.343692 0.343692 0.013201 0.892772 0.622905 0.053169 0.083819 0.661805 0.124257 0.033322 0.007744 0.018456 0.043548 0.267816 0.013610 0.010680 0.006436 0.161580 0.024629 0.105331 0.079910 0.077315 0.032776 0.352986 0.091683 0.003157 0.005083 0.086931 0.692675 1.000000 0.009317 0.106075 0.006980 0.063345 0.039557 0.067368 0.009281 0.016699 0.005403 0.015258 0.086931 1.000000 0.589336 0.007062 0.016805 0.107778 0.204632 0.108609 0.268558 0.150319 0.026494 0.004032 0.080422 0.749621 0.963908 0.000745 0.005038 nan 0.156443
Long-term Liability to Current Assets 0.000778 0.021508 0.022241 0.018300 0.000522 0.000178 0.001967 0.002062 0.002061 0.000641 0.001882 0.001729 0.008636 0.024446 0.006090 0.016004 0.011322 0.011623 0.011509 0.019496 0.031188 0.002441 0.022299 0.047070 0.001783 0.001546 0.005114 0.004905 0.000507 0.016918 0.001303 0.001410 0.023165 0.001150 0.003252 0.006971 0.002491 0.006936 0.006936 0.003992 0.007554 0.001512 0.022794 0.022193 0.017873 0.018985 0.001859 0.003641 0.009397 0.026209 0.014774 0.001617 0.017067 0.001754 0.004957 0.032043 0.029544 0.002666 0.004567 0.001828 0.032819 0.042355 0.000724 0.001069 0.072485 0.001969 0.009317 1.000000 0.001115 0.000927 0.004543 0.014843 0.001042 0.000288 0.014466 0.000518 0.001150 0.072485 0.009317 0.022575 0.002679 0.004564 0.024814 0.003569 0.007067 0.003255 0.023702 0.004701 0.001301 0.000523 0.007529 0.000063 0.002160 0.003908 nan 0.014437
Retained Earnings to Total Assets 0.217779 0.650217 0.718013 0.673738 0.164579 0.163013 0.021280 0.036236 0.034573 0.021105 0.034777 0.083315 0.079153 0.223252 0.023919 0.212204 0.491365 0.492760 0.492734 0.492078 0.225557 0.004366 0.415226 0.473736 0.008902 0.021450 0.064121 0.064422 0.011965 0.099163 0.047416 0.025056 0.205944 0.015432 0.011885 0.000065 0.009129 0.235423 0.235423 0.010451 0.117913 0.021729 0.413869 0.483355 0.064067 0.124320 0.016678 0.010700 0.048772 0.100233 0.001481 0.016047 0.233582 0.001538 0.217189 0.094297 0.065605 0.096487 0.015316 0.025724 0.198553 0.264403 0.001636 0.016548 0.027070 0.076849 0.106075 0.001115 1.000000 0.020517 0.541559 0.002107 0.007653 0.006343 0.007398 0.010140 0.002027 0.027070 0.106075 0.073713 0.189642 0.086905 0.371488 0.105862 0.174742 0.160774 0.794189 0.170156 0.013460 0.164583 0.247707 0.109810 0.013766 0.009603 nan 0.042936
Total income/Total expense 0.007137 0.023450 0.028873 0.024436 0.043608 0.043610 0.002047 0.003322 0.002985 0.001701 0.003094 0.001955 0.008301 0.026204 0.000202 0.000944 0.023143 0.023136 0.023117 0.023013 0.011126 0.000558 0.015660 0.022244 0.000307 0.000806 0.002600 0.002599 0.000653 0.020221 0.000709 0.000794 0.004849 0.003411 0.000314 0.000430 0.000725 0.027995 0.027995 0.052586 0.007016 0.000202 0.015657 0.023754 0.010417 0.014172 0.000684 0.459146 0.011172 0.006679 0.010976 0.000969 0.124769 0.000804 0.013297 0.023305 0.029451 0.006558 0.000274 0.001625 0.021716 0.036077 0.000249 0.001274 0.013303 0.003233 0.006980 0.000927 0.020517 1.000000 0.022219 0.000485 0.002178 0.000034 0.001812 0.000466 0.000429 0.013303 0.006980 0.005368 0.002000 0.000818 0.018124 0.000969 0.115768 0.001636 0.027065 0.001687 0.017818 0.043609 0.006213 0.007383 0.001156 0.000088 nan 0.031325
Total expense/Assets 0.139049 0.296019 0.357147 0.322223 0.225479 0.226170 0.005401 0.004525 0.002803 0.022281 0.004472 0.249426 0.049438 0.081517 0.010339 0.081476 0.235573 0.234878 0.235062 0.177996 0.013919 0.017885 0.070742 0.156954 0.050982 0.004173 0.031487 0.032827 0.013009 0.079619 0.042600 0.015412 0.060663 0.011762 0.018296 0.013466 0.015686 0.037513 0.037513 0.018443 0.022642 0.007404 0.069488 0.174815 0.037055 0.171386 0.018303 0.040179 0.064959 0.117786 0.114822 0.012086 0.146846 0.010872 0.108738 0.242834 0.194399 0.184944 0.012822 0.033007 0.115737 0.072308 0.018227 0.006701 0.192153 0.008948 0.063345 0.004543 0.541559 0.022219 1.000000 0.026547 0.003683 0.003656 0.008046 0.000045 0.011092 0.192153 0.063345 0.003318 0.127591 0.045883 0.120342 0.090256 0.012881 0.136084 0.470498 0.107944 0.004314 0.225478 0.190700 0.050501 0.017607 0.005122 nan 0.007907
Current Asset Turnover Rate 0.011929 0.005716 0.000869 0.002611 0.121275 0.121320 0.008117 0.008065 0.008174 0.003545 0.007518 0.170776 0.046460 0.007870 0.009759 0.063944 0.017123 0.016616 0.016690 0.000412 0.042716 0.010894 0.008130 0.009443 0.019388 0.011499 0.005805 0.005591 0.002073 0.000739 0.008879 0.005130 0.037312 0.002240 0.014514 0.030494 0.011118 0.124880 0.124880 0.015572 0.028854 0.001941 0.008815 0.008198 0.041233 0.353826 0.019486 0.021400 0.177299 0.042103 0.271284 0.007217 0.006812 0.013957 0.184527 0.082370 0.111142 0.125317 0.003691 0.002886 0.094912 0.022532 0.008716 0.012131 0.038198 0.048945 0.039557 0.014843 0.002107 0.000485 0.026547 1.000000 0.423768 0.003783 0.072658 0.001836 0.005134 0.038198 0.039557 0.032434 0.054514 0.030324 0.035322 0.033221 0.127562 0.005382 0.005032 0.005085 0.012551 0.121277 0.015505 0.043730 0.003415 0.025886 nan 0.101157
Quick Asset Turnover Rate 0.025814 0.027280 0.025143 0.029928 0.129715 0.129747 0.012696 0.012206 0.012368 0.006365 0.011440 0.153936 0.034643 0.057385 0.026821 0.046586 0.043038 0.042926 0.043121 0.029352 0.028332 0.016476 0.001263 0.024310 0.003755 0.012411 0.014418 0.014146 0.007177 0.032340 0.006129 0.009262 0.022286 0.007291 0.007069 0.007865 0.016815 0.203370 0.203370 0.001652 0.063473 0.000955 0.000433 0.023197 0.112787 0.252558 0.029472 0.003969 0.014567 0.043957 0.209559 0.010915 0.047514 0.015921 0.208682 0.217281 0.084330 0.234160 0.001284 0.026529 0.162418 0.077777 0.001269 0.029148 0.039084 0.049088 0.067368 0.001042 0.007653 0.002178 0.003683 0.423768 1.000000 0.004605 0.156490 0.002763 0.007765 0.039084 0.067368 0.041667 0.095772 0.055200 0.042236 0.058753 0.127015 0.004659 0.023834 0.001023 0.024169 0.129714 0.017511 0.068305 0.001264 0.009416 nan 0.156954
Working capitcal Turnover Rate 0.002894 0.001824 0.004491 0.002488 0.020451 0.020536 0.229568 0.090689 0.244911 0.742290 0.110703 0.003331 0.002909 0.044478 0.000607 0.002915 0.006438 0.006426 0.006431 0.002192 0.000240 0.184916 0.000435 0.001388 0.000023 0.005974 0.000371 0.000198 0.000385 0.004511 0.000367 0.001643 0.003031 0.000487 0.002905 0.000240 0.015300 0.031715 0.031715 0.031637 0.004406 0.001101 0.000332 0.001825 0.000594 0.006442 0.074727 0.000392 0.004528 0.000966 0.004805 0.039606 0.006489 0.000003 0.044792 0.011016 0.017572 0.014822 0.000310 0.000070 0.035566 0.030672 0.000116 0.003880 0.026895 0.016589 0.009281 0.000288 0.006343 0.000034 0.003656 0.003783 0.004605 1.000000 0.004215 0.948194 0.001218 0.026895 0.009281 0.001277 0.049727 0.051763 0.001734 0.033145 0.197348 0.001229 0.005680 0.000122 0.005063 0.020450 0.002406 0.007808 0.000342 0.000004 nan 0.077424
Cash Turnover Rate 0.018035 0.029477 0.025817 0.030410 0.071579 0.071321 0.016485 0.015581 0.015792 0.008805 0.014596 0.040730 0.070369 0.093887 0.019243 0.044051 0.054775 0.053797 0.053930 0.034256 0.058218 0.021618 0.012514 0.029827 0.009126 0.014003 0.014097 0.013462 0.006509 0.064419 0.002081 0.018438 0.041483 0.003635 0.022126 0.002048 0.016561 0.138934 0.138934 0.014006 0.004608 0.000351 0.012464 0.031204 0.049393 0.060722 0.022508 0.014979 0.017862 0.039828 0.041663 0.014322 0.011304 0.012579 0.140998 0.106712 0.044491 0.287559 0.001503 0.044147 0.126370 0.101758 0.002589 0.017556 0.006543 0.020252 0.016699 0.014466 0.007398 0.001812 0.008046 0.072658 0.156490 0.004215 1.000000 0.003385 0.004830 0.006543 0.016699 0.000606 0.144087 0.101751 0.069359 0.074053 0.073448 0.006498 0.016255 0.008560 0.023444 0.071578 0.009240 0.013463 0.000143 0.003773 nan 0.145710
Cash Flow to Sales 0.000479 0.011759 0.012198 0.011977 0.041559 0.041604 0.084747 0.233675 0.379952 0.677230 0.254886 0.003082 0.003677 0.016086 0.000173 0.004227 0.009424 0.009398 0.009403 0.007255 0.004107 0.037165 0.005120 0.006428 0.000182 0.005175 0.000916 0.000756 0.000180 0.004524 0.000039 0.000610 0.006674 0.000101 0.000025 0.000038 0.004189 0.018201 0.018201 0.008499 0.000476 0.000541 0.005206 0.006864 0.003459 0.007064 0.052668 0.000073 0.000165 0.001644 0.004045 0.019829 0.000762 0.000169 0.036276 0.014316 0.017334 0.013979 0.000109 0.000009 0.024671 0.013312 0.000085 0.002274 0.022191 0.014279 0.005403 0.000518 0.010140 0.000466 0.000045 0.001836 0.002763 0.948194 0.003385 1.000000 0.000049 0.022191 0.005403 0.000895 0.065010 0.037196 0.008168 0.043021 0.202509 0.000065 0.011634 0.000182 0.003314 0.041560 0.002661 0.003770 0.000208 0.000083 nan 0.022476
Fixed Assets to Assets 0.066328 0.009192 0.005860 0.008364 0.003507 0.003524 0.000106 0.000047 0.000003 0.000355 0.000016 0.007464 0.009092 0.006933 0.001840 0.010045 0.003112 0.003094 0.003107 0.006477 0.023987 0.000311 0.006732 0.006482 0.000301 0.000042 0.000381 0.000376 0.000043 0.006570 0.000166 0.000206 0.034956 0.000147 0.000415 0.013903 0.000318 0.023418 0.023418 0.393705 0.015588 0.000599 0.006723 0.006716 0.035281 0.016059 0.000557 0.006008 0.007658 0.004931 0.008856 0.000206 0.004646 0.142653 0.001406 0.021731 0.025904 0.010716 0.000254 0.048718 0.032646 0.008369 0.001658 0.001161 0.013908 0.007193 0.015258 0.001150 0.002027 0.000429 0.011092 0.005134 0.007765 0.001218 0.004830 0.000049 1.000000 0.013908 0.015258 0.002922 0.003535 0.001378 0.021983 0.003228 0.001686 0.000415 0.004380 0.000599 0.006361 0.003506 0.000700 0.010509 0.000825 0.002169 nan 0.007758
Current Liability to Liability 0.020809 0.052783 0.080401 0.046694 0.063547 0.064657 0.020520 0.019009 0.013732 0.011739 0.014602 0.013653 0.007120 0.068729 0.025009 0.064182 0.044689 0.044624 0.044538 0.107310 0.006296 0.027977 0.135996 0.106568 0.016519 0.011871 0.000929 0.000933 0.003392 0.044135 0.016129 0.027073 0.011324 0.004915 0.028012 0.022008 0.006780 0.082322 0.082322 0.074428 0.075594 0.004683 0.135385 0.102487 0.149187 0.321993 0.012795 0.024454 0.014910 0.285401 0.194031 0.027657 0.125118 0.023199 0.251502 0.462973 0.565640 0.243615 0.024001 0.008926 0.422185 0.062602 0.010507 0.000652 1.000000 0.071259 0.086931 0.072485 0.027070 0.013303 0.192153 0.038198 0.039084 0.026895 0.006543 0.022191 0.013908 1.000000 0.086931 0.234454 0.054809 0.046433 0.003067 0.041194 0.039736 0.007157 0.066277 0.034229 0.016296 0.063546 0.017171 0.017927 0.017911 0.009450 nan 0.098930
Current Liability to Equity 0.153828 0.142734 0.133816 0.142879 0.080422 0.080350 0.001860 0.002202 0.003196 0.008969 0.002689 0.035899 0.037452 0.087567 0.018880 0.015970 0.102098 0.102539 0.102431 0.094966 0.045022 0.006360 0.055461 0.089174 0.004456 0.005973 0.007517 0.007521 0.006179 0.036283 0.055279 0.001407 0.167594 0.003582 0.014777 0.004542 0.009967 0.343692 0.343692 0.013201 0.892772 0.622905 0.053169 0.083819 0.661805 0.124257 0.033322 0.007744 0.018456 0.043548 0.267816 0.013610 0.010680 0.006436 0.161580 0.024629 0.105331 0.079910 0.077315 0.032776 0.352986 0.091683 0.003157 0.005083 0.086931 0.692675 1.000000 0.009317 0.106075 0.006980 0.063345 0.039557 0.067368 0.009281 0.016699 0.005403 0.015258 0.086931 1.000000 0.589336 0.007062 0.016805 0.107778 0.204632 0.108609 0.268558 0.150319 0.026494 0.004032 0.080422 0.749621 0.963908 0.000745 0.005038 nan 0.156443
Equity to Long-term Liability 0.139014 0.086535 0.103015 0.083190 0.068810 0.068763 0.000654 0.007929 0.006326 0.014377 0.003093 0.024837 0.012524 0.040178 0.038229 0.054426 0.089004 0.090725 0.090667 0.114381 0.037488 0.010574 0.094285 0.110478 0.004258 0.012554 0.018494 0.018514 0.008501 0.013855 0.071401 0.011811 0.018924 0.002434 0.003287 0.008543 0.006328 0.244974 0.244974 0.021716 0.806889 0.462858 0.094417 0.111974 0.486097 0.020655 0.008364 0.007994 0.016432 0.053104 0.223240 0.031914 0.051603 0.008978 0.139294 0.115150 0.103652 0.095230 0.005047 0.012988 0.045339 0.058352 0.001960 0.009926 0.234454 0.353136 0.589336 0.022575 0.073713 0.005368 0.003318 0.032434 0.041667 0.001277 0.000606 0.000895 0.002922 0.234454 0.589336 1.000000 0.027925 0.016939 0.047268 0.218999 0.129499 0.055805 0.120242 0.006302 0.001439 0.068809 0.615905 0.778135 0.002936 0.007973 nan 0.110885
Cash Flow to Total Assets 0.070456 0.262454 0.263591 0.258428 0.098097 0.098056 0.020918 0.041845 0.046314 0.033285 0.042328 0.007630 0.008293 0.224786 0.012599 0.030302 0.142925 0.142930 0.143017 0.222378 0.246791 0.015799 0.218511 0.224392 0.004178 0.008680 0.027411 0.026884 0.000208 0.104641 0.008237 0.038569 0.247814 0.006635 0.008936 0.011678 0.022285 0.066502 0.066502 0.027432 0.026453 0.013533 0.216378 0.218673 0.025453 0.093131 0.012486 0.002480 0.015477 0.052169 0.044739 0.004929 0.075271 0.002807 0.233958 0.254929 0.202907 0.480174 0.001020 0.020220 0.037780 0.273598 0.005108 0.023234 0.054809 0.065755 0.007062 0.002679 0.189642 0.002000 0.127591 0.054514 0.095772 0.049727 0.144087 0.065010 0.003535 0.054809 0.007062 0.027925 1.000000 0.712655 0.332471 0.589998 0.115009 0.022924 0.254898 0.016782 0.011693 0.098099 0.050483 0.015893 0.002991 0.000176 nan 0.034015
Cash Flow to Liability 0.043125 0.159699 0.157065 0.157022 0.114138 0.114060 0.004669 0.011517 0.012243 0.011813 0.014059 0.006762 0.008771 0.364812 0.011341 0.023895 0.076980 0.076917 0.076871 0.123403 0.129250 0.156966 0.118816 0.123983 0.002393 0.003389 0.015736 0.015741 0.000120 0.064552 0.001937 0.019516 0.136652 0.005370 0.004089 0.004043 0.149285 0.076983 0.076983 0.052517 0.022185 0.004790 0.117421 0.122515 0.023877 0.037509 0.022651 0.003105 0.000805 0.025605 0.001048 0.001922 0.053627 0.000084 0.176265 0.161456 0.133968 0.353270 0.001393 0.008704 0.053637 0.382088 0.001131 0.020917 0.046433 0.042518 0.016805 0.004564 0.086905 0.000818 0.045883 0.030324 0.055200 0.051763 0.101751 0.037196 0.001378 0.046433 0.016805 0.016939 0.712655 1.000000 0.214116 0.320613 0.071694 0.005520 0.142567 0.014123 0.015018 0.114140 0.026664 0.019213 0.002400 0.001836 nan 0.109775
CFO to Assets 0.115383 0.504311 0.443017 0.497042 0.226990 0.226912 0.026682 0.031813 0.029454 0.000973 0.030653 0.005426 0.073629 0.603305 0.011482 0.103101 0.230814 0.230629 0.230342 0.333636 0.715003 0.002226 0.338206 0.308200 0.046844 0.002719 0.014993 0.013393 0.001262 0.100864 0.018421 0.023001 0.738276 0.001195 0.023447 0.008880 0.023123 0.268159 0.268159 0.031758 0.117624 0.011931 0.337872 0.318991 0.168028 0.018741 0.026659 0.004308 0.033695 0.020639 0.057832 0.009930 0.091023 0.008625 0.131677 0.084853 0.059645 0.281832 0.018524 0.029893 0.252778 0.702937 0.010441 0.026108 0.003067 0.016175 0.107778 0.024814 0.371488 0.018124 0.120342 0.035322 0.042236 0.001734 0.069359 0.008168 0.021983 0.003067 0.107778 0.047268 0.332471 0.214116 1.000000 0.181298 0.099646 0.035913 0.440095 0.076041 0.007769 0.226990 0.107850 0.098545 0.003771 0.006057 nan 0.113629
Cash Flow to Equity 0.058563 0.129002 0.112929 0.123622 0.030672 0.030676 0.014088 0.026245 0.030022 0.018515 0.027140 0.014722 0.008972 0.097761 0.006995 0.021563 0.072982 0.073000 0.073080 0.129661 0.199675 0.003660 0.126165 0.105591 0.005539 0.020307 0.006925 0.007324 0.000834 0.048385 0.013669 0.022967 0.089089 0.002907 0.005717 0.011266 0.006953 0.003178 0.003178 0.011582 0.193543 0.295827 0.132425 0.130916 0.159920 0.052024 0.005331 0.000835 0.016848 0.026669 0.084083 0.004661 0.018552 0.000878 0.101122 0.148667 0.120343 0.277303 0.006788 0.014241 0.027142 0.119707 0.008403 0.016766 0.041194 0.256678 0.204632 0.003569 0.105862 0.000969 0.090256 0.033221 0.058753 0.033145 0.074053 0.043021 0.003228 0.041194 0.204632 0.218999 0.589998 0.320613 0.181298 1.000000 0.063386 0.117402 0.120228 0.024583 0.005883 0.030672 0.180666 0.231107 0.001471 0.000239 nan 0.003321
Current Liability to Current Assets 0.171306 0.160725 0.195673 0.162572 0.132650 0.132607 0.079679 0.138584 0.166453 0.084875 0.140264 0.015511 0.065204 0.126473 0.000022 0.053579 0.164367 0.165083 0.165011 0.154690 0.052804 0.006224 0.145741 0.148721 0.002813 0.021073 0.033598 0.033395 0.009174 0.027703 0.117590 0.008920 0.052971 0.347630 0.006420 0.014302 0.025966 0.428180 0.428180 0.021608 0.124908 0.084231 0.144231 0.153719 0.027166 0.087297 0.021219 0.068197 0.054892 0.142589 0.126417 0.027678 0.050780 0.007121 0.625560 0.339550 0.354198 0.286691 0.202883 0.044378 0.351832 0.139718 0.002946 0.125024 0.039736 0.176390 0.108609 0.007067 0.174742 0.115768 0.012881 0.127562 0.127015 0.197348 0.073448 0.202509 0.001686 0.039736 0.108609 0.129499 0.115009 0.071694 0.099646 0.063386 1.000000 0.247158 0.202751 0.012853 0.049342 0.132652 0.070354 0.132372 0.022033 0.007652 nan 0.262199
Liability-Assets Flag 0.139212 0.109272 0.156890 0.120680 0.032930 0.032920 0.000295 0.003163 0.002746 0.005652 0.001233 0.004119 0.009960 0.013501 0.033571 0.028425 0.096776 0.096506 0.096527 0.105522 0.037403 0.000881 0.046381 0.104995 0.002142 0.046146 0.050839 0.050639 0.002059 0.027766 0.400342 0.009823 0.118872 0.000415 0.001173 0.001530 0.000899 0.203155 0.203155 0.005833 0.259702 0.005567 0.046447 0.109981 0.204500 0.012599 0.001575 0.001314 0.011811 0.004789 0.172578 0.000583 0.030241 0.001310 0.149085 0.000663 0.008918 0.012224 0.187510 0.002496 0.184388 0.016716 0.002435 0.023507 0.007157 0.160307 0.268558 0.003255 0.160774 0.001636 0.136084 0.005382 0.004659 0.001229 0.006498 0.000065 0.000415 0.007157 0.268558 0.055805 0.022924 0.005520 0.035913 0.117402 0.247158 1.000000 0.169104 0.032788 0.023927 0.032932 0.192688 0.229559 0.001717 0.000974 nan 0.027573
Net Income to Total Assets 0.315457 0.887670 0.961552 0.912040 0.300143 0.298155 0.028482 0.048587 0.045390 0.028423 0.045600 0.071365 0.079169 0.281309 0.048735 0.231210 0.493776 0.493803 0.493822 0.691152 0.292252 0.008315 0.577846 0.671748 0.003064 0.041046 0.119596 0.119870 0.024257 0.080031 0.072408 0.062183 0.252716 0.014946 0.017779 0.004969 0.008056 0.281422 0.281422 0.016566 0.177781 0.037822 0.575833 0.683623 0.094770 0.188774 0.025062 0.011002 0.052668 0.122505 0.013776 0.014309 0.306356 0.002153 0.297217 0.176086 0.133053 0.195652 0.008154 0.034389 0.214085 0.341188 0.000992 0.003639 0.066277 0.123817 0.150319 0.023702 0.794189 0.027065 0.470498 0.005032 0.023834 0.005680 0.016255 0.011634 0.004380 0.066277 0.150319 0.120242 0.254898 0.142567 0.440095 0.120228 0.202751 0.169104 1.000000 0.105201 0.011942 0.300146 0.328492 0.159697 0.010463 0.012746 nan 0.073916
Total assets to GNP price 0.035104 0.071725 0.098900 0.089088 0.022672 0.022750 0.003338 0.004243 0.003786 0.000408 0.004623 0.025524 0.020166 0.052766 0.007519 0.023643 0.059970 0.059780 0.059826 0.033509 0.023591 0.001272 0.032299 0.028837 0.002692 0.000063 0.001185 0.001166 0.000307 0.038909 0.000679 0.006583 0.041889 0.000599 0.001694 0.000586 0.001298 0.041055 0.041055 0.003589 0.011083 0.002446 0.032403 0.029308 0.027074 0.041944 0.113731 0.001897 0.009172 0.013477 0.059178 0.000843 0.017804 0.001892 0.008452 0.028238 0.033760 0.019727 0.001036 0.003604 0.056086 0.066037 0.000455 0.002637 0.034229 0.003121 0.026494 0.004701 0.170156 0.001687 0.107944 0.005085 0.001023 0.000122 0.008560 0.000182 0.000599 0.034229 0.026494 0.006302 0.016782 0.014123 0.076041 0.024583 0.012853 0.032788 0.105201 1.000000 0.000584 0.022673 0.040217 0.021982 0.001881 0.000239 nan 0.014871
No-credit Interval 0.005547 0.008135 0.011463 0.007523 0.004205 0.004038 0.000199 0.000075 0.001091 0.000637 0.000556 0.006497 0.006838 0.013642 0.003175 0.011488 0.014303 0.014424 0.014335 0.003791 0.002721 0.027256 0.001169 0.008267 0.000764 0.000180 0.002108 0.002026 0.002108 0.013174 0.010080 0.000310 0.008558 0.008178 0.014929 0.000076 0.002556 0.050218 0.050218 0.003389 0.004183 0.001531 0.000975 0.007939 0.021709 0.007536 0.004030 0.004210 0.010280 0.007192 0.019169 0.033598 0.009823 0.000702 0.038209 0.028876 0.002520 0.026421 0.039538 0.006208 0.053657 0.010303 0.001040 0.002553 0.016296 0.009143 0.004032 0.001301 0.013460 0.017818 0.004314 0.012551 0.024169 0.005063 0.023444 0.003314 0.006361 0.016296 0.004032 0.001439 0.011693 0.015018 0.007769 0.005883 0.049342 0.023927 0.011942 0.000584 1.000000 0.004203 0.000127 0.003724 0.008812 0.001027 nan 0.050609
Gross Profit to Sales 0.100044 0.334721 0.326971 0.333750 1.000000 0.999518 0.005746 0.032494 0.027176 0.051437 0.029431 0.206354 0.016975 0.341186 0.017198 0.067971 0.144662 0.145032 0.145058 0.256723 0.163190 0.117044 0.267946 0.247791 0.014172 0.022866 0.054639 0.053430 0.009122 0.016014 0.017450 0.026544 0.122675 0.024946 0.001381 0.002365 0.022363 0.245461 0.245461 0.006020 0.085732 0.022258 0.267412 0.248106 0.086720 0.099661 0.082343 0.022529 0.047666 0.001240 0.136158 0.019021 0.224980 0.006952 0.246308 0.152850 0.094785 0.241945 0.003205 0.030900 0.198028 0.246833 0.035024 0.035217 0.063546 0.067970 0.080422 0.000523 0.164583 0.043609 0.225478 0.121277 0.129714 0.020450 0.071578 0.041560 0.003506 0.063546 0.080422 0.068809 0.098099 0.114140 0.226990 0.030672 0.132652 0.032932 0.300146 0.022673 0.004203 1.000000 0.075303 0.085434 0.011806 0.001169 nan 0.120027
Net Income to Stockholder's Equity 0.180987 0.274287 0.291744 0.280617 0.075304 0.074891 0.006216 0.011343 0.010648 0.007693 0.011191 0.029733 0.021490 0.057933 0.010950 0.077920 0.148693 0.148872 0.148906 0.222961 0.074250 0.001104 0.183601 0.218389 0.001952 0.007570 0.020203 0.020273 0.006638 0.032565 0.068054 0.019467 0.162473 0.002489 0.002374 0.003604 0.000700 0.123986 0.123986 0.008679 0.806478 0.352618 0.181150 0.215690 0.422238 0.041242 0.006587 0.004093 0.026296 0.013604 0.034699 0.006549 0.096235 0.000679 0.109766 0.048011 0.038880 0.045075 0.021419 0.008188 0.092725 0.069212 0.000176 0.004009 0.017171 0.585971 0.749621 0.007529 0.247707 0.006213 0.190700 0.015505 0.017511 0.002406 0.009240 0.002661 0.000700 0.017171 0.749621 0.615905 0.050483 0.026664 0.107850 0.180666 0.070354 0.192688 0.328492 0.040217 0.000127 0.075303 1.000000 0.791836 0.000093 0.005147 nan 0.029622
Liability to Equity 0.166812 0.143629 0.141039 0.142838 0.085434 0.085407 0.001541 0.004043 0.004390 0.011899 0.002996 0.034809 0.035363 0.080773 0.003423 0.030002 0.110850 0.111797 0.111682 0.114114 0.047298 0.002132 0.077102 0.107727 0.001687 0.000537 0.011685 0.011705 0.007433 0.033052 0.068649 0.005198 0.133686 0.003741 0.009645 0.006926 0.010045 0.349250 0.349250 0.001791 0.955857 0.621808 0.075374 0.104149 0.670373 0.084243 0.023753 0.003221 0.008359 0.015134 0.273553 0.022624 0.011415 0.002286 0.177645 0.024486 0.039449 0.097849 0.061051 0.029935 0.286398 0.091214 0.001788 0.007637 0.017927 0.650474 0.963908 0.000063 0.109810 0.007383 0.050501 0.043730 0.068305 0.007808 0.013463 0.003770 0.010509 0.017927 0.963908 0.778135 0.015893 0.019213 0.098545 0.231107 0.132372 0.229559 0.159697 0.021982 0.003724 0.085434 0.791836 1.000000 0.002119 0.001487 nan 0.159654
Degree of Financial Leverage (DFL) 0.010508 0.016575 0.011515 0.014663 0.011806 0.011268 0.000935 0.000855 0.000927 0.000556 0.000774 0.013577 0.013945 0.006348 0.007301 0.014962 0.021860 0.021781 0.021674 0.018829 0.006200 0.001140 0.015936 0.017885 0.000672 0.001247 0.002030 0.002014 0.000014 0.005520 0.000697 0.000310 0.003717 0.000574 0.000083 0.016829 0.001262 0.017982 0.017982 0.004319 0.007260 0.001061 0.016052 0.018358 0.006647 0.020769 0.002111 0.001342 0.001189 0.018565 0.009233 0.001084 0.006094 0.001859 0.036716 0.030419 0.031049 0.024289 0.000876 0.001636 0.006987 0.008961 0.002294 0.000344 0.017911 0.011134 0.000745 0.002160 0.013766 0.001156 0.017607 0.003415 0.001264 0.000342 0.000143 0.000208 0.000825 0.017911 0.000745 0.002936 0.002991 0.002400 0.003771 0.001471 0.022033 0.001717 0.010463 0.001881 0.008812 0.011806 0.000093 0.002119 1.000000 0.016513 nan 0.016739
Interest Coverage Ratio (Interest expense to EBIT) 0.005509 0.010573 0.013372 0.011473 0.001167 0.001158 0.000393 0.000984 0.000957 0.001024 0.000798 0.006232 0.012160 0.001262 0.000779 0.030275 0.002175 0.002358 0.002328 0.008039 0.001358 0.000053 0.006331 0.008143 0.000327 0.004576 0.005373 0.005329 0.001086 0.001723 0.000446 0.001498 0.005525 0.000150 0.001867 0.034321 0.000431 0.012571 0.012571 0.000342 0.001776 0.000620 0.006258 0.008172 0.006764 0.019358 0.000497 0.000892 0.011491 0.000537 0.017437 0.002667 0.003220 0.000957 0.008912 0.000886 0.007281 0.007205 0.000209 0.003422 0.021428 0.001268 0.023608 0.005260 0.009450 0.002063 0.005038 0.003908 0.009603 0.000088 0.005122 0.025886 0.009416 0.000004 0.003773 0.000083 0.002169 0.009450 0.005038 0.007973 0.000176 0.001836 0.006057 0.000239 0.007652 0.000974 0.012746 0.000239 0.001027 0.001169 0.005147 0.001487 0.016513 1.000000 nan 0.008339
Net Income Flag nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Equity to Liability 0.083048 0.052416 0.057887 0.056430 0.120029 0.120196 0.017071 0.014559 0.010900 0.012293 0.011299 0.120763 0.045244 0.331710 0.028945 0.053148 0.098434 0.098721 0.098390 0.036722 0.052117 0.233203 0.009316 0.028185 0.002302 0.001725 0.001253 0.001383 0.014498 0.015962 0.010685 0.002454 0.045546 0.010228 0.017084 0.012626 0.338898 0.625879 0.625879 0.104344 0.146012 0.018260 0.009528 0.031292 0.205183 0.198485 0.010629 0.002744 0.006726 0.044538 0.226456 0.011295 0.030261 0.000081 0.369149 0.095978 0.015311 0.299732 0.012449 0.038004 0.506360 0.398769 0.005719 0.004448 0.098930 0.058512 0.156443 0.014437 0.042936 0.031325 0.007907 0.101157 0.156954 0.077424 0.145710 0.022476 0.007758 0.098930 0.156443 0.110885 0.034015 0.109775 0.113629 0.003321 0.262199 0.027573 0.073916 0.014871 0.050609 0.120027 0.029622 0.159654 0.016739 0.008339 nan 1.000000
In [ ]:
cor_matrix = prueba2.corr().abs()   # esta versión permite colorear aquellas correlaciones que nos llaman la atención tanto positivas como negativas
cor_matrix.style.background_gradient(sns.light_palette('red', as_cmap=True))   # código tomado de la web en que aplican este método, es muy útil ayuda cuando hay muchas variables
Out[ ]:
Bankrupt? ROA(C) before interest and depreciation before interest Operating Profit Rate Operating Profit Growth Rate Total Asset Growth Rate Quick Ratio Total Asset Turnover Accounts Receivable Turnover Inventory Turnover Rate (times) Current Asset Turnover Rate Cash Turnover Rate Cash Flow to Liability Gross Profit to Sales Net Income Flag
Bankrupt? 1.000000 0.260807 0.000230 0.015168 0.044431 0.025058 0.067915 0.004754 0.001376 0.011929 0.018035 0.043125 0.100044 nan
ROA(C) before interest and depreciation before interest 0.260807 1.000000 0.035725 0.036511 0.019635 0.026336 0.210622 0.033947 0.062660 0.005716 0.029477 0.159699 0.334721 nan
Operating Profit Rate 0.000230 0.035725 1.000000 0.004952 0.034465 0.000323 0.029456 0.023171 0.009576 0.008117 0.016485 0.004669 0.005746 nan
Operating Profit Growth Rate 0.015168 0.036511 0.004952 1.000000 0.015553 0.000404 0.044088 0.025684 0.002095 0.011499 0.014003 0.003389 0.022866 nan
Total Asset Growth Rate 0.044431 0.019635 0.034465 0.015553 1.000000 0.013451 0.072393 0.030866 0.030277 0.000739 0.064419 0.064552 0.016014 nan
Quick Ratio 0.025058 0.026336 0.000323 0.000404 0.013451 1.000000 0.037770 0.001574 0.019058 0.014514 0.022126 0.004089 0.001381 nan
Total Asset Turnover 0.067915 0.210622 0.029456 0.044088 0.072393 0.037770 1.000000 0.060229 0.152298 0.353826 0.060722 0.037509 0.099661 nan
Accounts Receivable Turnover 0.004754 0.033947 0.023171 0.025684 0.030866 0.001574 0.060229 1.000000 0.013201 0.019486 0.022508 0.022651 0.082343 nan
Inventory Turnover Rate (times) 0.001376 0.062660 0.009576 0.002095 0.030277 0.019058 0.152298 0.013201 1.000000 0.177299 0.017862 0.000805 0.047666 nan
Current Asset Turnover Rate 0.011929 0.005716 0.008117 0.011499 0.000739 0.014514 0.353826 0.019486 0.177299 1.000000 0.072658 0.030324 0.121277 nan
Cash Turnover Rate 0.018035 0.029477 0.016485 0.014003 0.064419 0.022126 0.060722 0.022508 0.017862 0.072658 1.000000 0.101751 0.071578 nan
Cash Flow to Liability 0.043125 0.159699 0.004669 0.003389 0.064552 0.004089 0.037509 0.022651 0.000805 0.030324 0.101751 1.000000 0.114140 nan
Gross Profit to Sales 0.100044 0.334721 0.005746 0.022866 0.016014 0.001381 0.099661 0.082343 0.047666 0.121277 0.071578 0.114140 1.000000 nan
Net Income Flag nan nan nan nan nan nan nan nan nan nan nan nan nan nan

Se observan correlaciones fuertes entre varias caracteristicas, en lo que respecta a las correlaciones posivitas , endonde si una aumenta la otra tambien se encuentra por ejemplo:

La característica que indica el comportamiento de las empesas Bankrupt?, presenta una correlación media co n las características del tipo de ROA before interest , presentando con las tres valores de 0.26 , 0.28 y 0.27

La característica que indica el comportamiento de las empesas Bankrupt?, presenta una correlación media con la característica Debt ratio %, con un valor de 0.25 Bankrupt?, presenta una correlación media con la característica Net worth/Assets con un valor de 0.25

Bankrupt?, presenta una correlación media con la característica Net profit before tax/Paid-in capital con un valor de 0.2

ROA(C) before interest and depreciation before interest junto a las caracteristicas similares que corresponden ROA(A) before interest and % after tax y ROA(B) before interest and depreciation after tax, presentan una correlación alta.

Realized Sales Gross Margin y la característica Operating Gross Margin tienen una correlación alta de 0.999

Persistent EPS in the Last Four Seasons tiene una correlacion alta con las characteristicas OA(C) before interest and depreciation before interest, ROA(A) before interest and % after tax y ROA(B) before interest and depreciation after tax, para valores de alrrededor de 0.76

Persistent EPS in the Last Four Seasons presenta una correlacion alta con las caracteristicas de Net Value Per Share, con un valor de 0.75

Operating profit/Paid-in capital presenta una correlacion alta con Persistent EPS in the Last Four Seasons

Net Income to Total Assets presenta alta correlacion con las características ROA (las tres características de similitud) con valores de 0.88, 096 y 0.91

Current Liabilities/Equity presenta una alta correlación con la característica de Borrowing dependency y Contingent liabilities/Net worth, con valors de 0.89 y 0.62 respectivamente

Cash Flow to Total Assets presenta una alta correlacion con Cash Flow to Liability y la caracteristica Cash Flow to Equity

Gráfica de Correlación

In [ ]:
corrdat = datos.iloc[:,[0,1,2,3,4,5,6,7,8,9]]
In [ ]:
f,ax = plt.subplots(figsize=(12,12))
sns.heatmap(corrdat.corr(method='spearman'),annot=True,vmin=-1, vmax=1, center= 0)
plt.show()
In [ ]:
corrdat1 = prueba2
In [ ]:
f,ax = plt.subplots(figsize=(12,12))
sns.heatmap(corrdat1.corr(method='spearman'),annot=True,vmin=-1, vmax=1, center= 0)
plt.show()
In [ ]:
corrdat2= datos.iloc[:,[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36]]
In [ ]:
f,ax = plt.subplots(figsize=(15,15))
sns.heatmap(corrdat2.corr(method='spearman'),annot=True,vmin=-1, vmax=1, center= 0)
plt.show()

ROA(C) before interest and depreciation before interest junto a las caracteristicas similares que corresponden ROA(A) before interest and % after tax y ROA(B) before interest and depreciation after tax, presentan una correlación alta.

Realized Sales Gross Margin y la característica Operating Gross Margin tienen una correlación alta de 0.999

Persistent EPS in the Last Four Seasons tiene una correlacion alta con las characteristicas OA(C) before interest and depreciation before interest, ROA(A) before interest and % after tax y ROA(B) before interest and depreciation after tax, para valores de alrrededor de 0.76

Persistent EPS in the Last Four Seasons presenta una correlacion alta con las caracteristicas de Net Value Per Share, con un valor de 0.75

Operating profit/Paid-in capital presenta una correlacion alta con Persistent EPS in the Last Four Seasons

Net Income to Total Assets presenta alta correlacion con las características ROA (las tres características de similitud) con valores de 0.88, 096 y 0.91

Current Liabilities/Equity presenta una alta correlación con la característica de Borrowing dependency y Contingent liabilities/Net worth, con valors de 0.89 y 0.62 respectivamente

Cash Flow to Total Assets presenta una alta correlacion con Cash Flow to Liability y la caracteristica Cash Flow to Equity

In [ ]:
f,ax = plt.subplots(figsize=(60,60))
sns.heatmap(datos.corr(method='spearman'),annot=True,vmin=-1, vmax=1, center= 0)
plt.show()

Grafico de Scattermatrix

In [ ]:
from pandas.plotting import scatter_matrix 
scatter_matrix((datos[['Bankrupt?','ROA(C) before interest and depreciation before interest','ROA(A) before interest and % after tax','ROA(B) before interest and depreciation after tax','Operating Gross Margin','Realized Sales Gross Margin','Operating Profit Rate','Pre-tax net Interest Rate','After-tax net Interest Rate','Non-industry income and expenditure/revenue']]),figsize = (20, 20));

ANOVA

Por el tipo de variables, el cálculo de los ANOVAS y PV values, no aportarian resultados manejables o para interpretar, por lo tanto no se proceden a calcular

Modelo de Machine Learning

In [ ]:
datos
Out[ ]:
Bankrupt? ROA(C) before interest and depreciation before interest ROA(A) before interest and % after tax ROA(B) before interest and depreciation after tax Operating Gross Margin Realized Sales Gross Margin Operating Profit Rate Pre-tax net Interest Rate After-tax net Interest Rate Non-industry income and expenditure/revenue Continuous interest rate (after tax) Operating Expense Rate Research and development expense rate Cash flow rate Interest-bearing debt interest rate Tax rate (A) Net Value Per Share (B) Net Value Per Share (A) Net Value Per Share (C) Persistent EPS in the Last Four Seasons Cash Flow Per Share Revenue Per Share (Yuan ¥) Operating Profit Per Share (Yuan ¥) Per Share Net profit before tax (Yuan ¥) Realized Sales Gross Profit Growth Rate Operating Profit Growth Rate After-tax Net Profit Growth Rate Regular Net Profit Growth Rate Continuous Net Profit Growth Rate Total Asset Growth Rate Net Value Growth Rate Total Asset Return Growth Rate Ratio Cash Reinvestment % Current Ratio Quick Ratio Interest Expense Ratio Total debt/Total net worth Debt ratio % Net worth/Assets Long-term fund suitability ratio (A) ... Current Assets/Total Assets Cash/Total Assets Quick Assets/Current Liability Cash/Current Liability Current Liability to Assets Operating Funds to Liability Inventory/Working Capital Inventory/Current Liability Current Liabilities/Liability Working Capital/Equity Current Liabilities/Equity Long-term Liability to Current Assets Retained Earnings to Total Assets Total income/Total expense Total expense/Assets Current Asset Turnover Rate Quick Asset Turnover Rate Working capitcal Turnover Rate Cash Turnover Rate Cash Flow to Sales Fixed Assets to Assets Current Liability to Liability Current Liability to Equity Equity to Long-term Liability Cash Flow to Total Assets Cash Flow to Liability CFO to Assets Cash Flow to Equity Current Liability to Current Assets Liability-Assets Flag Net Income to Total Assets Total assets to GNP price No-credit Interval Gross Profit to Sales Net Income to Stockholder's Equity Liability to Equity Degree of Financial Leverage (DFL) Interest Coverage Ratio (Interest expense to EBIT) Net Income Flag Equity to Liability
0 1 0.370594 0.424389 0.405750 0.601457 0.601457 0.998969 0.796887 0.808809 0.302646 0.780985 1.256969e-04 0.000000e+00 0.458143 7.250725e-04 0.000000 0.147950 0.147950 0.147950 0.169141 0.311664 0.017560 0.095921 0.138736 0.022102 0.848195 0.688979 0.688979 0.217535 4.980000e+09 0.000327 0.263100 0.363725 0.002259 0.001208 0.629951 0.021266 0.207576 0.792424 0.005024 ... 0.190643 0.004094 0.001997 1.473360e-04 0.147308 0.334015 0.276920 0.001036 0.676269 0.721275 0.339077 2.559237e-02 0.903225 0.002022 0.064856 7.010000e+08 6.550000e+09 0.593831 4.580000e+08 0.671568 0.424206 0.676269 0.339077 0.126549 0.637555 0.458609 0.520382 0.312905 0.118250 0 0.716845 0.009219 0.622879 0.601453 0.827890 0.290202 0.026601 0.564050 1 0.016469
1 1 0.464291 0.538214 0.516730 0.610235 0.610235 0.998946 0.797380 0.809301 0.303556 0.781506 2.897851e-04 0.000000e+00 0.461867 6.470647e-04 0.000000 0.182251 0.182251 0.182251 0.208944 0.318137 0.021144 0.093722 0.169918 0.022080 0.848088 0.689693 0.689702 0.217620 6.110000e+09 0.000443 0.264516 0.376709 0.006016 0.004039 0.635172 0.012502 0.171176 0.828824 0.005059 ... 0.182419 0.014948 0.004136 1.383910e-03 0.056963 0.341106 0.289642 0.005210 0.308589 0.731975 0.329740 2.394682e-02 0.931065 0.002226 0.025516 1.065198e-04 7.700000e+09 0.593916 2.490000e+09 0.671570 0.468828 0.308589 0.329740 0.120916 0.641100 0.459001 0.567101 0.314163 0.047775 0 0.795297 0.008323 0.623652 0.610237 0.839969 0.283846 0.264577 0.570175 1 0.020794
2 1 0.426071 0.499019 0.472295 0.601450 0.601364 0.998857 0.796403 0.808388 0.302035 0.780284 2.361297e-04 2.550000e+07 0.458521 7.900790e-04 0.000000 0.177911 0.177911 0.193713 0.180581 0.307102 0.005944 0.092338 0.142803 0.022760 0.848094 0.689463 0.689470 0.217601 7.280000e+09 0.000396 0.264184 0.368913 0.011543 0.005348 0.629631 0.021248 0.207516 0.792484 0.005100 ... 0.602806 0.000991 0.006302 5.340000e+09 0.098162 0.336731 0.277456 0.013879 0.446027 0.742729 0.334777 3.715116e-03 0.909903 0.002060 0.021387 1.791094e-03 1.022676e-03 0.594502 7.610000e+08 0.671571 0.276179 0.446027 0.334777 0.117922 0.642765 0.459254 0.538491 0.314515 0.025346 0 0.774670 0.040003 0.623841 0.601449 0.836774 0.290189 0.026555 0.563706 1 0.016474
3 1 0.399844 0.451265 0.457733 0.583541 0.583541 0.998700 0.796967 0.808966 0.303350 0.781241 1.078888e-04 0.000000e+00 0.465705 4.490449e-04 0.000000 0.154187 0.154187 0.154187 0.193722 0.321674 0.014368 0.077762 0.148603 0.022046 0.848005 0.689110 0.689110 0.217568 4.880000e+09 0.000382 0.263371 0.384077 0.004194 0.002896 0.630228 0.009572 0.151465 0.848535 0.005047 ... 0.225815 0.018851 0.002961 1.010646e-03 0.098715 0.348716 0.276580 0.003540 0.615848 0.729825 0.331509 2.216520e-02 0.906902 0.001831 0.024161 8.140000e+09 6.050000e+09 0.593889 2.030000e+09 0.671519 0.559144 0.615848 0.331509 0.120760 0.579039 0.448518 0.604105 0.302382 0.067250 0 0.739555 0.003252 0.622929 0.583538 0.834697 0.281721 0.026697 0.564663 1 0.023982
4 1 0.465022 0.538432 0.522298 0.598783 0.598783 0.998973 0.797366 0.809304 0.303475 0.781550 7.890000e+09 0.000000e+00 0.462746 6.860686e-04 0.000000 0.167502 0.167502 0.167502 0.212537 0.319162 0.029690 0.096898 0.168412 0.022096 0.848258 0.689697 0.689697 0.217626 5.510000e+09 0.000439 0.265218 0.379690 0.006022 0.003727 0.636055 0.005150 0.106509 0.893491 0.005303 ... 0.358380 0.014161 0.004275 6.804636e-04 0.110195 0.344639 0.287913 0.004869 0.975007 0.732000 0.330726 0.000000e+00 0.913850 0.002224 0.026385 6.680000e+09 5.050000e+09 0.593915 8.240000e+08 0.671563 0.309555 0.975007 0.330726 0.110933 0.622374 0.454411 0.578469 0.311567 0.047725 0 0.795016 0.003878 0.623521 0.598782 0.839973 0.278514 0.024752 0.575617 1 0.035490
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
6814 0 0.493687 0.539468 0.543230 0.604455 0.604462 0.998992 0.797409 0.809331 0.303510 0.781588 1.510213e-04 4.500000e+09 0.463734 1.790179e-04 0.113372 0.175045 0.175045 0.175045 0.216602 0.320966 0.020766 0.098200 0.172102 0.022374 0.848205 0.689778 0.689778 0.217635 7.070000e+09 0.000450 0.264517 0.380155 0.010451 0.005457 0.631415 0.006655 0.124618 0.875382 0.005150 ... 0.578455 0.099481 0.005469 5.071548e-03 0.103838 0.346224 0.277543 0.013212 0.786888 0.736716 0.330914 1.792237e-03 0.925611 0.002266 0.019060 2.294154e-04 1.244230e-04 0.593985 1.077940e-04 0.671570 0.400338 0.786888 0.330914 0.112622 0.639806 0.458639 0.587178 0.314063 0.027951 0 0.799927 0.000466 0.623620 0.604455 0.840359 0.279606 0.027064 0.566193 1 0.029890
6815 0 0.475162 0.538269 0.524172 0.598308 0.598308 0.998992 0.797414 0.809327 0.303520 0.781586 5.220000e+09 1.440000e+09 0.461978 2.370237e-04 0.371596 0.181324 0.181324 0.181324 0.216697 0.318278 0.023050 0.098608 0.172780 0.022159 0.848245 0.689734 0.689734 0.217631 5.220000e+09 0.000445 0.264730 0.377389 0.009259 0.006741 0.631489 0.004623 0.099253 0.900747 0.006772 ... 0.444043 0.080337 0.006790 4.727181e-03 0.089901 0.342166 0.277368 0.006730 0.849898 0.734584 0.329753 2.204673e-03 0.932629 0.002288 0.011118 1.517299e-04 1.173396e-04 0.593954 7.710000e+09 0.671572 0.096136 0.849898 0.329753 0.112329 0.642072 0.459058 0.569498 0.314446 0.031470 0 0.799748 0.001959 0.623931 0.598306 0.840306 0.278132 0.027009 0.566018 1 0.038284
6816 0 0.472725 0.533744 0.520638 0.610444 0.610213 0.998984 0.797401 0.809317 0.303512 0.781546 2.509312e-04 1.039086e-04 0.472189 0.000000e+00 0.490839 0.269521 0.269521 0.269521 0.210929 0.324857 0.044255 0.100073 0.173232 0.022068 0.847978 0.689202 0.689202 0.217547 5.990000e+09 0.000435 0.263858 0.379392 0.038424 0.035112 0.630612 0.001392 0.038939 0.961061 0.009149 ... 0.496053 0.412885 0.035531 8.821248e-02 0.024414 0.358847 0.277022 0.007810 0.553964 0.737432 0.326921 0.000000e+00 0.932000 0.002239 0.035446 1.762272e-04 1.749713e-04 0.594025 4.074263e-04 0.671564 0.055509 0.553964 0.326921 0.110933 0.631678 0.452465 0.589341 0.313353 0.007542 0 0.797778 0.002840 0.624156 0.610441 0.840138 0.275789 0.026791 0.565158 1 0.097649
6817 0 0.506264 0.559911 0.554045 0.607850 0.607850 0.999074 0.797500 0.809399 0.303498 0.781663 1.236154e-04 2.510000e+09 0.476123 2.110211e-04 0.181294 0.213392 0.213392 0.213392 0.228326 0.346573 0.031535 0.111799 0.185584 0.022350 0.854064 0.696113 0.696113 0.218006 7.250000e+09 0.000529 0.264409 0.401028 0.012782 0.007256 0.630731 0.003816 0.086979 0.913021 0.005529 ... 0.564439 0.112238 0.007753 7.133218e-03 0.083199 0.380251 0.277353 0.013334 0.893241 0.736713 0.329294 3.200000e+09 0.939613 0.002395 0.016443 2.135940e-04 1.351937e-04 0.593997 1.165392e-04 0.671606 0.246805 0.893241 0.329294 0.110957 0.684857 0.471313 0.678338 0.320118 0.022916 0 0.811808 0.002837 0.623957 0.607846 0.841084 0.277547 0.026822 0.565302 1 0.044009
6818 0 0.493053 0.570105 0.549548 0.627409 0.627409 0.998080 0.801987 0.813800 0.313415 0.786079 1.431695e-03 0.000000e+00 0.427721 5.900000e+08 0.000000 0.220766 0.220766 0.220766 0.227758 0.305793 0.000665 0.092501 0.182119 0.025316 0.848053 0.689527 0.689527 0.217605 9.350000e+09 0.000519 0.264186 0.360102 0.051348 0.040897 0.630618 0.000461 0.014149 0.985851 0.058476 ... 0.505010 0.238147 0.051481 6.667354e-02 0.018517 0.239585 0.276975 0.000000 1.000000 0.737286 0.326690 0.000000e+00 0.938005 0.002791 0.006089 7.863781e-03 8.238471e-03 0.598674 9.505992e-03 0.672096 0.005016 1.000000 0.326690 0.110933 0.659917 0.483285 0.505531 0.316238 0.005579 0 0.815956 0.000707 0.626680 0.627408 0.841019 0.275114 0.026793 0.565167 1 0.233902

6819 rows × 96 columns

In [ ]:
datos1= datos.copy()
In [ ]:
datos1
Out[ ]:
Bankrupt? ROA(C) before interest and depreciation before interest ROA(A) before interest and % after tax ROA(B) before interest and depreciation after tax Operating Gross Margin Realized Sales Gross Margin Operating Profit Rate Pre-tax net Interest Rate After-tax net Interest Rate Non-industry income and expenditure/revenue Continuous interest rate (after tax) Operating Expense Rate Research and development expense rate Cash flow rate Interest-bearing debt interest rate Tax rate (A) Net Value Per Share (B) Net Value Per Share (A) Net Value Per Share (C) Persistent EPS in the Last Four Seasons Cash Flow Per Share Revenue Per Share (Yuan ¥) Operating Profit Per Share (Yuan ¥) Per Share Net profit before tax (Yuan ¥) Realized Sales Gross Profit Growth Rate Operating Profit Growth Rate After-tax Net Profit Growth Rate Regular Net Profit Growth Rate Continuous Net Profit Growth Rate Total Asset Growth Rate Net Value Growth Rate Total Asset Return Growth Rate Ratio Cash Reinvestment % Current Ratio Quick Ratio Interest Expense Ratio Total debt/Total net worth Debt ratio % Net worth/Assets Long-term fund suitability ratio (A) ... Current Assets/Total Assets Cash/Total Assets Quick Assets/Current Liability Cash/Current Liability Current Liability to Assets Operating Funds to Liability Inventory/Working Capital Inventory/Current Liability Current Liabilities/Liability Working Capital/Equity Current Liabilities/Equity Long-term Liability to Current Assets Retained Earnings to Total Assets Total income/Total expense Total expense/Assets Current Asset Turnover Rate Quick Asset Turnover Rate Working capitcal Turnover Rate Cash Turnover Rate Cash Flow to Sales Fixed Assets to Assets Current Liability to Liability Current Liability to Equity Equity to Long-term Liability Cash Flow to Total Assets Cash Flow to Liability CFO to Assets Cash Flow to Equity Current Liability to Current Assets Liability-Assets Flag Net Income to Total Assets Total assets to GNP price No-credit Interval Gross Profit to Sales Net Income to Stockholder's Equity Liability to Equity Degree of Financial Leverage (DFL) Interest Coverage Ratio (Interest expense to EBIT) Net Income Flag Equity to Liability
0 1 0.370594 0.424389 0.405750 0.601457 0.601457 0.998969 0.796887 0.808809 0.302646 0.780985 1.256969e-04 0.000000e+00 0.458143 7.250725e-04 0.000000 0.147950 0.147950 0.147950 0.169141 0.311664 0.017560 0.095921 0.138736 0.022102 0.848195 0.688979 0.688979 0.217535 4.980000e+09 0.000327 0.263100 0.363725 0.002259 0.001208 0.629951 0.021266 0.207576 0.792424 0.005024 ... 0.190643 0.004094 0.001997 1.473360e-04 0.147308 0.334015 0.276920 0.001036 0.676269 0.721275 0.339077 2.559237e-02 0.903225 0.002022 0.064856 7.010000e+08 6.550000e+09 0.593831 4.580000e+08 0.671568 0.424206 0.676269 0.339077 0.126549 0.637555 0.458609 0.520382 0.312905 0.118250 0 0.716845 0.009219 0.622879 0.601453 0.827890 0.290202 0.026601 0.564050 1 0.016469
1 1 0.464291 0.538214 0.516730 0.610235 0.610235 0.998946 0.797380 0.809301 0.303556 0.781506 2.897851e-04 0.000000e+00 0.461867 6.470647e-04 0.000000 0.182251 0.182251 0.182251 0.208944 0.318137 0.021144 0.093722 0.169918 0.022080 0.848088 0.689693 0.689702 0.217620 6.110000e+09 0.000443 0.264516 0.376709 0.006016 0.004039 0.635172 0.012502 0.171176 0.828824 0.005059 ... 0.182419 0.014948 0.004136 1.383910e-03 0.056963 0.341106 0.289642 0.005210 0.308589 0.731975 0.329740 2.394682e-02 0.931065 0.002226 0.025516 1.065198e-04 7.700000e+09 0.593916 2.490000e+09 0.671570 0.468828 0.308589 0.329740 0.120916 0.641100 0.459001 0.567101 0.314163 0.047775 0 0.795297 0.008323 0.623652 0.610237 0.839969 0.283846 0.264577 0.570175 1 0.020794
2 1 0.426071 0.499019 0.472295 0.601450 0.601364 0.998857 0.796403 0.808388 0.302035 0.780284 2.361297e-04 2.550000e+07 0.458521 7.900790e-04 0.000000 0.177911 0.177911 0.193713 0.180581 0.307102 0.005944 0.092338 0.142803 0.022760 0.848094 0.689463 0.689470 0.217601 7.280000e+09 0.000396 0.264184 0.368913 0.011543 0.005348 0.629631 0.021248 0.207516 0.792484 0.005100 ... 0.602806 0.000991 0.006302 5.340000e+09 0.098162 0.336731 0.277456 0.013879 0.446027 0.742729 0.334777 3.715116e-03 0.909903 0.002060 0.021387 1.791094e-03 1.022676e-03 0.594502 7.610000e+08 0.671571 0.276179 0.446027 0.334777 0.117922 0.642765 0.459254 0.538491 0.314515 0.025346 0 0.774670 0.040003 0.623841 0.601449 0.836774 0.290189 0.026555 0.563706 1 0.016474
3 1 0.399844 0.451265 0.457733 0.583541 0.583541 0.998700 0.796967 0.808966 0.303350 0.781241 1.078888e-04 0.000000e+00 0.465705 4.490449e-04 0.000000 0.154187 0.154187 0.154187 0.193722 0.321674 0.014368 0.077762 0.148603 0.022046 0.848005 0.689110 0.689110 0.217568 4.880000e+09 0.000382 0.263371 0.384077 0.004194 0.002896 0.630228 0.009572 0.151465 0.848535 0.005047 ... 0.225815 0.018851 0.002961 1.010646e-03 0.098715 0.348716 0.276580 0.003540 0.615848 0.729825 0.331509 2.216520e-02 0.906902 0.001831 0.024161 8.140000e+09 6.050000e+09 0.593889 2.030000e+09 0.671519 0.559144 0.615848 0.331509 0.120760 0.579039 0.448518 0.604105 0.302382 0.067250 0 0.739555 0.003252 0.622929 0.583538 0.834697 0.281721 0.026697 0.564663 1 0.023982
4 1 0.465022 0.538432 0.522298 0.598783 0.598783 0.998973 0.797366 0.809304 0.303475 0.781550 7.890000e+09 0.000000e+00 0.462746 6.860686e-04 0.000000 0.167502 0.167502 0.167502 0.212537 0.319162 0.029690 0.096898 0.168412 0.022096 0.848258 0.689697 0.689697 0.217626 5.510000e+09 0.000439 0.265218 0.379690 0.006022 0.003727 0.636055 0.005150 0.106509 0.893491 0.005303 ... 0.358380 0.014161 0.004275 6.804636e-04 0.110195 0.344639 0.287913 0.004869 0.975007 0.732000 0.330726 0.000000e+00 0.913850 0.002224 0.026385 6.680000e+09 5.050000e+09 0.593915 8.240000e+08 0.671563 0.309555 0.975007 0.330726 0.110933 0.622374 0.454411 0.578469 0.311567 0.047725 0 0.795016 0.003878 0.623521 0.598782 0.839973 0.278514 0.024752 0.575617 1 0.035490
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
6814 0 0.493687 0.539468 0.543230 0.604455 0.604462 0.998992 0.797409 0.809331 0.303510 0.781588 1.510213e-04 4.500000e+09 0.463734 1.790179e-04 0.113372 0.175045 0.175045 0.175045 0.216602 0.320966 0.020766 0.098200 0.172102 0.022374 0.848205 0.689778 0.689778 0.217635 7.070000e+09 0.000450 0.264517 0.380155 0.010451 0.005457 0.631415 0.006655 0.124618 0.875382 0.005150 ... 0.578455 0.099481 0.005469 5.071548e-03 0.103838 0.346224 0.277543 0.013212 0.786888 0.736716 0.330914 1.792237e-03 0.925611 0.002266 0.019060 2.294154e-04 1.244230e-04 0.593985 1.077940e-04 0.671570 0.400338 0.786888 0.330914 0.112622 0.639806 0.458639 0.587178 0.314063 0.027951 0 0.799927 0.000466 0.623620 0.604455 0.840359 0.279606 0.027064 0.566193 1 0.029890
6815 0 0.475162 0.538269 0.524172 0.598308 0.598308 0.998992 0.797414 0.809327 0.303520 0.781586 5.220000e+09 1.440000e+09 0.461978 2.370237e-04 0.371596 0.181324 0.181324 0.181324 0.216697 0.318278 0.023050 0.098608 0.172780 0.022159 0.848245 0.689734 0.689734 0.217631 5.220000e+09 0.000445 0.264730 0.377389 0.009259 0.006741 0.631489 0.004623 0.099253 0.900747 0.006772 ... 0.444043 0.080337 0.006790 4.727181e-03 0.089901 0.342166 0.277368 0.006730 0.849898 0.734584 0.329753 2.204673e-03 0.932629 0.002288 0.011118 1.517299e-04 1.173396e-04 0.593954 7.710000e+09 0.671572 0.096136 0.849898 0.329753 0.112329 0.642072 0.459058 0.569498 0.314446 0.031470 0 0.799748 0.001959 0.623931 0.598306 0.840306 0.278132 0.027009 0.566018 1 0.038284
6816 0 0.472725 0.533744 0.520638 0.610444 0.610213 0.998984 0.797401 0.809317 0.303512 0.781546 2.509312e-04 1.039086e-04 0.472189 0.000000e+00 0.490839 0.269521 0.269521 0.269521 0.210929 0.324857 0.044255 0.100073 0.173232 0.022068 0.847978 0.689202 0.689202 0.217547 5.990000e+09 0.000435 0.263858 0.379392 0.038424 0.035112 0.630612 0.001392 0.038939 0.961061 0.009149 ... 0.496053 0.412885 0.035531 8.821248e-02 0.024414 0.358847 0.277022 0.007810 0.553964 0.737432 0.326921 0.000000e+00 0.932000 0.002239 0.035446 1.762272e-04 1.749713e-04 0.594025 4.074263e-04 0.671564 0.055509 0.553964 0.326921 0.110933 0.631678 0.452465 0.589341 0.313353 0.007542 0 0.797778 0.002840 0.624156 0.610441 0.840138 0.275789 0.026791 0.565158 1 0.097649
6817 0 0.506264 0.559911 0.554045 0.607850 0.607850 0.999074 0.797500 0.809399 0.303498 0.781663 1.236154e-04 2.510000e+09 0.476123 2.110211e-04 0.181294 0.213392 0.213392 0.213392 0.228326 0.346573 0.031535 0.111799 0.185584 0.022350 0.854064 0.696113 0.696113 0.218006 7.250000e+09 0.000529 0.264409 0.401028 0.012782 0.007256 0.630731 0.003816 0.086979 0.913021 0.005529 ... 0.564439 0.112238 0.007753 7.133218e-03 0.083199 0.380251 0.277353 0.013334 0.893241 0.736713 0.329294 3.200000e+09 0.939613 0.002395 0.016443 2.135940e-04 1.351937e-04 0.593997 1.165392e-04 0.671606 0.246805 0.893241 0.329294 0.110957 0.684857 0.471313 0.678338 0.320118 0.022916 0 0.811808 0.002837 0.623957 0.607846 0.841084 0.277547 0.026822 0.565302 1 0.044009
6818 0 0.493053 0.570105 0.549548 0.627409 0.627409 0.998080 0.801987 0.813800 0.313415 0.786079 1.431695e-03 0.000000e+00 0.427721 5.900000e+08 0.000000 0.220766 0.220766 0.220766 0.227758 0.305793 0.000665 0.092501 0.182119 0.025316 0.848053 0.689527 0.689527 0.217605 9.350000e+09 0.000519 0.264186 0.360102 0.051348 0.040897 0.630618 0.000461 0.014149 0.985851 0.058476 ... 0.505010 0.238147 0.051481 6.667354e-02 0.018517 0.239585 0.276975 0.000000 1.000000 0.737286 0.326690 0.000000e+00 0.938005 0.002791 0.006089 7.863781e-03 8.238471e-03 0.598674 9.505992e-03 0.672096 0.005016 1.000000 0.326690 0.110933 0.659917 0.483285 0.505531 0.316238 0.005579 0 0.815956 0.000707 0.626680 0.627408 0.841019 0.275114 0.026793 0.565167 1 0.233902

6819 rows × 96 columns

In [ ]:
X = datos1  # Renombrando variable para utilizarla en Scikit-Learn
In [ ]:
# Normalizando dataframe
scaler = StandardScaler()
X_std = scaler.fit_transform(X)
In [ ]:
# Importando PCA
pca = PCA()
pca.fit(X_std)
Out[ ]:
PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,
    svd_solver='auto', tol=0.0, whiten=False)
In [ ]:
evr = pca.explained_variance_ratio_
evr
Out[ ]:
array([1.33042166e-01, 7.14465593e-02, 4.98367820e-02, 4.67479217e-02,
       4.19014322e-02, 3.20225577e-02, 3.01736399e-02, 2.90447308e-02,
       2.77447020e-02, 2.17370993e-02, 2.04660876e-02, 1.96530569e-02,
       1.77015032e-02, 1.58731924e-02, 1.56520395e-02, 1.51904806e-02,
       1.50085780e-02, 1.38279479e-02, 1.30104538e-02, 1.27572697e-02,
       1.23498325e-02, 1.20564755e-02, 1.14881093e-02, 1.10985600e-02,
       1.10009084e-02, 1.08883190e-02, 1.06603272e-02, 1.06275640e-02,
       1.06004458e-02, 1.03706253e-02, 1.02927816e-02, 1.01646543e-02,
       1.00868539e-02, 1.00290756e-02, 9.88952674e-03, 9.76477906e-03,
       9.59715552e-03, 9.56999428e-03, 9.37446960e-03, 9.03690397e-03,
       8.94770210e-03, 8.74886056e-03, 8.59701472e-03, 8.39475742e-03,
       8.18583438e-03, 7.96219210e-03, 7.52916062e-03, 7.22034546e-03,
       6.91752439e-03, 6.29399599e-03, 5.94511957e-03, 5.68656898e-03,
       5.66684496e-03, 5.24580781e-03, 4.89225207e-03, 4.45266105e-03,
       4.33347345e-03, 4.04420593e-03, 3.41033176e-03, 3.40694816e-03,
       2.62782622e-03, 2.56043021e-03, 2.27329647e-03, 2.25703558e-03,
       1.77612159e-03, 1.74208075e-03, 1.62767247e-03, 1.47619233e-03,
       1.18112210e-03, 8.91541562e-04, 8.64170679e-04, 6.69021218e-04,
       5.62298582e-04, 3.92001545e-04, 3.06026450e-04, 2.58287211e-04,
       2.31118460e-04, 2.03051167e-04, 1.59682885e-04, 1.00078166e-04,
       5.90215198e-05, 3.96560211e-05, 2.79175422e-05, 1.69842487e-05,
       9.66065978e-06, 8.21442270e-06, 6.79796471e-06, 3.96684371e-06,
       1.56209477e-06, 7.80428573e-11, 1.11003792e-13, 2.10108393e-20,
       1.33946974e-31, 2.43467679e-33, 1.05919116e-33, 1.05919116e-33])
In [ ]:
# Ploteando grafico de Componentes principales
fig = plt.figure(figsize=(8,8))
plt.plot(range(1, len(X.columns)+1), evr.cumsum(), marker='o', linestyle=':')
plt.xlabel('Numero de Componentes', fontsize=18)
plt.ylabel('Varianza Acumulada Explicada',fontsize=18)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.show()
In [ ]:
# Iteracion para comprobar numero de componentes optimos a utilizar por su nivel de varianza

for i, exp_var in enumerate(evr.cumsum()):
    if exp_var >= 0.8:
        n_comps = i + 1
        break
print("Numero de Componentes Optimos:", n_comps)
pca = PCA(n_components=n_comps)
pca.fit(X_std)
scores_pca = pca.transform(X_std)
Numero de Componentes Optimos: 35

Algoritmo K-means

In [ ]:
# Encontrando el punto del codo de la curva de WCSS (dentro de la suma de cuadrados) usando el KneedLocator
wcss = []
max_clusters = 21
for i in range(1, max_clusters):
    kmeans_pca = KMeans(i, init='k-means++', random_state=42)
    kmeans_pca.fit(scores_pca)
    wcss.append(kmeans_pca.inertia_)
n_clusters = KneeLocator([i for i in range(1, max_clusters)], wcss, curve='convex', direction='decreasing').knee
print("Numero de Clusters Optimos:", n_clusters)
Numero de Clusters Optimos: 10
In [ ]:
# Ploteando grafico 
fig = plt.figure(figsize=(8,8))
plt.plot(range(1, 21), wcss, marker='o', linestyle=':')
plt.vlines(KneeLocator([i for i in range(1, max_clusters)], wcss, curve='convex', 
                       direction='decreasing').knee, ymin=min(wcss), ymax=max(wcss), linestyles='dashed')
plt.xlabel('Numero de Clusters', fontsize=18)
plt.ylabel('Dentro del cluster [Suma de cuadrados] (WCSS)', fontsize=18)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.show()

Analisis y Visualización

In [ ]:
# Creando la optimizacion de parametros con PCA y K-Means
kmeans_pca = KMeans(n_clusters=n_clusters, init='k-means++', random_state=42)
kmeans_pca.fit(scores_pca);
In [ ]:
# Etiquentando cada uno de los datos dentro del cluster respectivo
df_seg_pca_kmeans = pd.concat([pd.DataFrame(X.reset_index(drop=True)), pd.DataFrame(scores_pca)], axis=1)
df_seg_pca_kmeans.columns.values[(-1*n_comps):] = ["Component " + str(i+1) for i in range(n_comps)]
df_seg_pca_kmeans['Cluster'] = kmeans_pca.labels_
df_seg_pca_kmeans.head()
Out[ ]:
Bankrupt? ROA(C) before interest and depreciation before interest ROA(A) before interest and % after tax ROA(B) before interest and depreciation after tax Operating Gross Margin Realized Sales Gross Margin Operating Profit Rate Pre-tax net Interest Rate After-tax net Interest Rate Non-industry income and expenditure/revenue Continuous interest rate (after tax) Operating Expense Rate Research and development expense rate Cash flow rate Interest-bearing debt interest rate Tax rate (A) Net Value Per Share (B) Net Value Per Share (A) Net Value Per Share (C) Persistent EPS in the Last Four Seasons Cash Flow Per Share Revenue Per Share (Yuan ¥) Operating Profit Per Share (Yuan ¥) Per Share Net profit before tax (Yuan ¥) Realized Sales Gross Profit Growth Rate Operating Profit Growth Rate After-tax Net Profit Growth Rate Regular Net Profit Growth Rate Continuous Net Profit Growth Rate Total Asset Growth Rate Net Value Growth Rate Total Asset Return Growth Rate Ratio Cash Reinvestment % Current Ratio Quick Ratio Interest Expense Ratio Total debt/Total net worth Debt ratio % Net worth/Assets Long-term fund suitability ratio (A) ... Degree of Financial Leverage (DFL) Interest Coverage Ratio (Interest expense to EBIT) Net Income Flag Equity to Liability Component 1 Component 2 Component 3 Component 4 Component 5 Component 6 Component 7 Component 8 Component 9 Component 10 Component 11 Component 12 Component 13 Component 14 Component 15 Component 16 Component 17 Component 18 Component 19 Component 20 Component 21 Component 22 Component 23 Component 24 Component 25 Component 26 Component 27 Component 28 Component 29 Component 30 Component 31 Component 32 Component 33 Component 34 Component 35 Cluster
0 1 0.370594 0.424389 0.405750 0.601457 0.601457 0.998969 0.796887 0.808809 0.302646 0.780985 1.256969e-04 0.0 0.458143 0.000725 0.0 0.147950 0.147950 0.147950 0.169141 0.311664 0.017560 0.095921 0.138736 0.022102 0.848195 0.688979 0.688979 0.217535 4.980000e+09 0.000327 0.263100 0.363725 0.002259 0.001208 0.629951 0.021266 0.207576 0.792424 0.005024 ... 0.026601 0.564050 1 0.016469 -7.805385 0.516755 0.316821 1.005600 -0.165988 1.906849 0.749536 -1.301348 0.684049 -2.591946 1.040055 0.534247 -0.197499 0.233276 0.152870 -0.420766 -0.739194 -1.040804 -0.172282 -0.509989 0.269950 0.233043 0.358533 0.294776 0.055357 0.425093 -0.469265 0.717464 0.634181 -0.074917 0.990184 0.731924 0.437998 0.359262 0.756928 2
1 1 0.464291 0.538214 0.516730 0.610235 0.610235 0.998946 0.797380 0.809301 0.303556 0.781506 2.897851e-04 0.0 0.461867 0.000647 0.0 0.182251 0.182251 0.182251 0.208944 0.318137 0.021144 0.093722 0.169918 0.022080 0.848088 0.689693 0.689702 0.217620 6.110000e+09 0.000443 0.264516 0.376709 0.006016 0.004039 0.635172 0.012502 0.171176 0.828824 0.005059 ... 0.264577 0.570175 1 0.020794 -3.179078 -0.819671 2.164440 2.456478 -0.839434 1.076363 0.373279 -1.477938 0.562413 -1.348285 -0.975509 -0.511172 0.629219 0.674510 -0.301165 -0.817046 -0.061883 -0.280227 0.215342 -0.530065 -0.753279 1.080102 1.388762 -4.597421 -4.195829 -1.326468 -4.020746 -4.031619 9.225463 -2.884475 0.362752 -0.590495 -3.037268 6.716925 3.026120 5
2 1 0.426071 0.499019 0.472295 0.601450 0.601364 0.998857 0.796403 0.808388 0.302035 0.780284 2.361297e-04 25500000.0 0.458521 0.000790 0.0 0.177911 0.177911 0.193713 0.180581 0.307102 0.005944 0.092338 0.142803 0.022760 0.848094 0.689463 0.689470 0.217601 7.280000e+09 0.000396 0.264184 0.368913 0.011543 0.005348 0.629631 0.021248 0.207516 0.792484 0.005100 ... 0.026555 0.563706 1 0.016474 -4.782747 -0.244321 0.318487 0.712846 -0.338954 0.286307 0.041091 -0.705122 0.694396 -2.449746 -2.023327 0.806143 -0.029850 1.355108 -1.008365 -2.696270 0.935297 -0.641379 3.986302 -4.040414 0.486287 1.166294 0.368235 2.494207 1.503442 0.601751 0.032526 0.975839 0.428325 0.490839 -0.115213 -1.010403 0.167731 1.998937 2.777527 2
3 1 0.399844 0.451265 0.457733 0.583541 0.583541 0.998700 0.796967 0.808966 0.303350 0.781241 1.078888e-04 0.0 0.465705 0.000449 0.0 0.154187 0.154187 0.154187 0.193722 0.321674 0.014368 0.077762 0.148603 0.022046 0.848005 0.689110 0.689110 0.217568 4.880000e+09 0.000382 0.263371 0.384077 0.004194 0.002896 0.630228 0.009572 0.151465 0.848535 0.005047 ... 0.026697 0.564663 1 0.023982 -6.294096 -1.236130 1.309161 2.143829 -0.734904 1.459084 -0.523375 0.094432 -0.537595 -1.193573 1.951623 0.232113 0.637712 0.664907 -0.142395 -0.949355 -0.175332 -0.258444 -0.042900 -0.535259 0.121002 1.303253 -0.988090 0.597273 -0.058131 0.742601 -0.835160 0.610785 1.316916 -0.232956 0.887198 1.176036 0.784558 -0.683179 1.177408 2
4 1 0.465022 0.538432 0.522298 0.598783 0.598783 0.998973 0.797366 0.809304 0.303475 0.781550 7.890000e+09 0.0 0.462746 0.000686 0.0 0.167502 0.167502 0.167502 0.212537 0.319162 0.029690 0.096898 0.168412 0.022096 0.848258 0.689697 0.689697 0.217626 5.510000e+09 0.000439 0.265218 0.379690 0.006022 0.003727 0.636055 0.005150 0.106509 0.893491 0.005303 ... 0.024752 0.575617 1 0.035490 -2.819900 -0.035578 -0.797929 0.878147 -0.206675 0.689811 -0.306965 0.089965 -0.429237 -0.063595 1.887583 1.294581 -0.170162 -0.262037 -0.009291 -0.106485 -1.011816 -0.792306 1.217720 -0.905663 -0.095651 0.449765 -0.110518 0.306741 -0.198693 0.637946 -0.748150 0.476912 1.175123 -0.863490 1.071844 0.714195 0.671245 -1.006258 1.071475 2

5 rows × 132 columns

Creando visualizacion de los datos con PCA

In [ ]:
# Creando visualizacion de los datos con PCA

x = df_seg_pca_kmeans['Component 2']
y = df_seg_pca_kmeans['Component 1']
fig = plt.figure(figsize=(10, 8))
sns.scatterplot(x, y, hue=df_seg_pca_kmeans['Cluster'], palette = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:cyan', 'tab:pink', 'tab:gray', 'tab:olive', 'goldenrod'])
plt.title('Clusters vistos con PCA', fontsize=20)
plt.xlabel("Componente 2", fontsize=18)
plt.ylabel("Componente 1", fontsize=18)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.show();

Ests aplicacion del modelo de clusterizacion se realizó con la selección automatica de las caracteristicas, los cluster o grupos generados no quedan claramente, se procederá a realizar una selección de caracteristicas y aplicación de limpieza de atipicos para observar los cambios en la clusterizacioón

In [ ]:
# Creando visualizacion de los datos con PCA

x = df_seg_pca_kmeans['Component 3']
y = df_seg_pca_kmeans['Component 4']
fig = plt.figure(figsize=(10, 8))
sns.scatterplot(x, y, hue=df_seg_pca_kmeans['Cluster'], palette = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:cyan', 'tab:pink', 'tab:gray', 'tab:olive','goldenrod'])
plt.title('Clusters vistos con PCA', fontsize=20)
plt.xlabel("Componente 3", fontsize=18)
plt.ylabel("Componente 4", fontsize=18)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.show();
In [ ]:
# Creando visualizacion de los datos con PCA

x = df_seg_pca_kmeans['Component 10']
y = df_seg_pca_kmeans['Component 9']
fig = plt.figure(figsize=(10, 8))
sns.scatterplot(x, y, hue=df_seg_pca_kmeans['Cluster'], palette = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:cyan', 'tab:pink', 'tab:gray', 'tab:olive','goldenrod'])
plt.title('Clusters vistos con PCA', fontsize=20)
plt.xlabel("Componente 10", fontsize=18)
plt.ylabel("Componente 9", fontsize=18)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.show();

Prueba de Modelo Cluster Con Caracteristicas Seleccionadas

In [1322]:
prueba3 = datos.iloc[:,[0,1,6,25,29,34,45,46,48,71,74,81,89,94]]
In [1323]:
prueba3.head(3)
Out[1323]:
Bankrupt? ROA_C Operating_Profit_Rate Operating_Profit_Growth_Rate Total_Asset_Growth_Rate Quick_Ratio Total Asset Turnover Accounts_Receivable_Turnover Inventory Turnover Rate (times) Current_Asset_Turnover_Rate Cash Turnover Rate Cash_Flow_to_Liability Gross Profit to Sales Net Income Flag
0 1 0.370594 0.998969 0.848195 4.980000e+09 0.001208 0.086957 0.001814 1.820926e-04 7.010000e+08 4.580000e+08 0.458609 0.601453 1
1 1 0.464291 0.998946 0.848088 6.110000e+09 0.004039 0.064468 0.001286 9.360000e+09 1.065198e-04 2.490000e+09 0.459001 0.610237 1
2 1 0.426071 0.998857 0.848094 7.280000e+09 0.005348 0.014993 0.001495 6.500000e+07 1.791094e-03 7.610000e+08 0.459254 0.601449 1
In [1332]:
# Renombrando variable para utilizarla en Scikit-Learn

X = prueba3
In [1333]:
# Normalizando dataframe
scaler = StandardScaler()
X_std = scaler.fit_transform(X)
In [1334]:
# Importando PCA

pca = PCA()
pca.fit(X_std)
Out[1334]:
PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,
    svd_solver='auto', tol=0.0, whiten=False)
In [1335]:
# El atributo muestra cuanta varianza es explicada por cada uno de las 13 variables
evr = pca.explained_variance_ratio_
evr
Out[1335]:
array([0.12317891, 0.11939467, 0.09048244, 0.07914099, 0.07727208,
       0.07667167, 0.07488617, 0.07195694, 0.06773481, 0.06601067,
       0.06360467, 0.05058042, 0.03908555, 0.        ])
In [1336]:
# Ploteando grafico de Componentes principales
fig = plt.figure(figsize=(8,8))
plt.plot(range(1, len(X.columns)+1), evr.cumsum(), marker='o', linestyle=':')
plt.xlabel('Numero de Componentes', fontsize=18)
plt.ylabel('Varianza Acumulada Explicada',fontsize=18)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.show()
In [1337]:
# Iteracion para comprobar numero de componentes optimos a utilizar por su nivel de varianza

for i, exp_var in enumerate(evr.cumsum()):
    if exp_var >= 0.8:
        n_comps = i + 1
        break
print("Numero de Componentes Optimos:", n_comps)
pca = PCA(n_components=n_comps)
pca.fit(X_std)
scores_pca = pca.transform(X_std)
Numero de Componentes Optimos: 10
In [1339]:
# Encontrando el punto del codo de la curva de WCSS (dentro de la suma de cuadrados) usando el KneedLocator
wcss = []
max_clusters = 21
for i in range(1, max_clusters):
    kmeans_pca = KMeans(i, init='k-means++', random_state=42)
    kmeans_pca.fit(scores_pca)
    wcss.append(kmeans_pca.inertia_)
n_clusters = KneeLocator([i for i in range(1, max_clusters)], wcss, curve='convex', direction='decreasing').knee
print("Numero de Clusters Optimos:", n_clusters)
Numero de Clusters Optimos: 9
In [1340]:
# Ploteando grafico 
fig = plt.figure(figsize=(8,8))
plt.plot(range(1, 21), wcss, marker='o', linestyle=':')
plt.vlines(KneeLocator([i for i in range(1, max_clusters)], wcss, curve='convex', 
                       direction='decreasing').knee, ymin=min(wcss), ymax=max(wcss), linestyles='dashed')
plt.xlabel('Numero de Clusters', fontsize=18)
plt.ylabel('Dentro del cluster [Suma de cuadrados] (WCSS)', fontsize=18)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.show()
In [1341]:
# Creando la optimizacion de parametros con PCA y K-Means
kmeans_pca = KMeans(n_clusters=n_clusters, init='k-means++', random_state=42)
kmeans_pca.fit(scores_pca);
In [1342]:
# Etiquentando cada uno de los datos dentro del cluster respectivo
df_seg_pca_kmeans = pd.concat([pd.DataFrame(X.reset_index(drop=True)), pd.DataFrame(scores_pca)], axis=1)
df_seg_pca_kmeans.columns.values[(-1*n_comps):] = ["Component " + str(i+1) for i in range(n_comps)]
df_seg_pca_kmeans['Cluster'] = kmeans_pca.labels_
df_seg_pca_kmeans.head()
Out[1342]:
Bankrupt? ROA_C Operating_Profit_Rate Operating_Profit_Growth_Rate Total_Asset_Growth_Rate Quick_Ratio Total Asset Turnover Accounts_Receivable_Turnover Inventory Turnover Rate (times) Current_Asset_Turnover_Rate Cash Turnover Rate Cash_Flow_to_Liability Gross Profit to Sales Net Income Flag Component 1 Component 2 Component 3 Component 4 Component 5 Component 6 Component 7 Component 8 Component 9 Component 10 Cluster
0 1 0.370594 0.998969 0.848195 4.980000e+09 0.001208 0.086957 0.001814 1.820926e-04 7.010000e+08 4.580000e+08 0.458609 0.601453 1 3.681109 1.247276 2.260981 -1.177269 -0.066989 -0.362859 -1.460034 -1.513249 1.415586 -2.664271 1
1 1 0.464291 0.998946 0.848088 6.110000e+09 0.004039 0.064468 0.001286 9.360000e+09 1.065198e-04 2.490000e+09 0.459001 0.610237 1 3.389758 -0.314367 0.779598 -1.347451 -0.749452 -0.338640 -1.354544 -0.163387 2.350388 -2.577807 1
2 1 0.426071 0.998857 0.848094 7.280000e+09 0.005348 0.014993 0.001495 6.500000e+07 1.791094e-03 7.610000e+08 0.459254 0.601449 1 3.521582 0.570953 1.631986 -1.076154 0.230512 -0.397503 -1.340254 -1.972257 1.670029 -2.796544 1
3 1 0.399844 0.998700 0.848005 4.880000e+09 0.002896 0.089955 0.001966 7.130000e+09 8.140000e+09 2.030000e+09 0.448518 0.583538 1 3.666680 2.603002 1.439512 -1.124117 -0.669127 -0.392123 -1.458827 -0.413356 2.319671 -1.675766 1
4 1 0.465022 0.998973 0.848258 5.510000e+09 0.003727 0.175412 0.001449 1.633674e-04 6.680000e+09 8.240000e+08 0.454411 0.598782 1 1.949130 2.586199 2.043451 -0.979521 0.108207 -0.433017 -1.461668 -1.525956 1.822428 -2.699567 1
In [1344]:
# Creando visualizacion de los datos con PCA

x = df_seg_pca_kmeans['Component 2']
y = df_seg_pca_kmeans['Component 1']
fig = plt.figure(figsize=(10, 8))
sns.scatterplot(x, y, hue=df_seg_pca_kmeans['Cluster'], palette = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:cyan', 'tab:pink', 'tab:gray', 'goldenrod'])
plt.title('Clusters vistos con PCA', fontsize=20)
plt.xlabel("Componente 2", fontsize=18)
plt.ylabel("Componente 1", fontsize=18)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.show();

Se observan cluster sobrepuestos así como no claramente definidos, se procederá a realizar la prueba de limpieza de los atipicos para observar el cambio en los cluster.

ML Supervisado

In [ ]:
# Importando bibliotecas
from pandas import read_csv 
import pandas as pd #manejo y estructuracion de datos y su manipulación
from pandas.plotting import scatter_matrix #diagramas de correlacción
from matplotlib import pyplot #Hacer gráficos en python
from sklearn.model_selection import train_test_split #lograr dividir las muestras
from sklearn.model_selection import cross_val_score #validación cruzada score 
from sklearn.model_selection import StratifiedKFold #validacion cruzada 
from sklearn.metrics import classification_report 
from sklearn.metrics import confusion_matrix #matriz de confusión
from sklearn.metrics import accuracy_score #score de validación cruzada
In [ ]:
# Modelos de ML con que se va a trabajar
from sklearn.metrics import accuracy_score #score de validación cruzada 
from sklearn.linear_model import LogisticRegression #regresion logística
from sklearn.tree import DecisionTreeClassifier #arboles de decision
from sklearn.neighbors import KNeighborsClassifier #KNN
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis #Análisis discriminante lineal 
from sklearn.naive_bayes import GaussianNB #Gauss Bayesiana
from sklearn.svm import SVC # Maquinas de Soporte Vectorial
from sklearn.model_selection import train_test_split

Crear un conjunto de datos de Validación

In [ ]:
# Conjunto de datos de validación dividida
# Con el 80% se crea el modelo y con el 20% se entrena

array = prueba2.values #los datos ahora se transforman en un arreglo

X = array[:,1:13]  # se toman los datos, sin la clase de clasificación, son 4 posiciones, por eso 4
y = array[:,0] # se toman los datos después de la posición 4, en este caso las clases.

# Se dividen los datos en conjunto de entrenamiento y prueba, se utiliza random_state = 0 para que no dé
# resultados diferentes si se vuelve a correr. 
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.20, random_state=0)

Se va a probar con los siguientes algoritmos:

  1. Regresión logística (LR)
  2. Análisis discriminante lineal (LDA)
  3. K Vecinos más cercanos (KNN).
  4. Árboles de clasificación y regresión (CART).
  5. Gauss Bayesiana (NB).
In [ ]:
# Algoritmos de Comprobación, se guardan en una lista
models = []
models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVC', SVC()))
In [ ]:
#Se debe seleccionar el mejor modelo, ya que ahora se tienen 6 modelos y estimaciones de precisión para cada uno,
#por ello se necesita comparar los modelos entre sí y seleccionar el más preciso.
import warnings
warnings.filterwarnings('ignore')

resultados = []
names = []


# Si se necesita tanto el índice o nombre, así como el elemento, se usa for indice, elemento en lista
for name, model in models:
    kfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True) # Declaracion de la validación cruzada, las características
    cv_resultados = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy') # genera la precisión de la validación cruzada y la guarda en la variable cv_resultados en lista
    resultados.append(cv_resultados)  # genera la precisión de la validación cruzada y la guarda en la variable cv_resultados en matrices, esto para hacer el boxplot.
    names.append(name) # names en matrices
    print('%s: %f (%f)' % (name, cv_resultados.mean(), cv_resultados.std()))
LR: 0.967920 (0.000899)
LDA: 0.965904 (0.003670)
KNN: 0.966821 (0.002366)
CART: 0.948123 (0.013037)
NB: 0.035380 (0.002721)
SVC: 0.968103 (0.000874)
In [ ]:
# vamos a elegir el SVC
In [ ]:
# Haciendo predicciones y evaluación del dataset

model = SVC()
model.fit(X_train, Y_train)
prediccion = model.predict(X_test)
In [ ]:
mc =pd.DataFrame(confusion_matrix(Y_test, prediccion, labels=[0,1]), 
                 index = [0,1],  
                 columns = [0,1])  
 
# Evaluando Predicciones
print("ROC:", accuracy_score(Y_test, prediccion),sep='\n')
print("")
print("Matriz de Confusión:", mc,sep='\n')
ROC:
0.966275659824047

Matriz de Confusión:
      0  1
0  1318  0
1    46  0
In [ ]:
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=10)
classifier.fit(X_train, Y_train)
Out[ ]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=10, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)
In [ ]:
y_pred = classifier.predict(X_test)
In [ ]:
cm = confusion_matrix(Y_test, y_pred)    # vemos una identificación de 1318  identificados correctamente, mientras que se identificaron 46 falss negativos
print(cm)
[[1318    0]
 [  46    0]]
In [ ]:
empresa1 =  classifier.predict([[0.37,0.95,0.85,5.667,0.003,0.002,0.0126,1.0667,5.45,3.85,0.385,0]])     # predicción
print(empresa1)
[0.]

Prueba Limpiando los Outliers

In [ ]:
prueba2.head(3)
Out[ ]:
Bankrupt? ROA_C Operating_Profit_Rate Operating Profit Growth Rate Total_Asset_Growth_Rate Quick Ratio Total Asset Turnover Accounts Receivable Turnover Inventory Turnover Rate (times) Current_Asset_Turnover_Rate Cash Turnover Rate Cash Flow to Liability Gross Profit to Sales Net Income Flag
0 1 0.370594 0.998969 0.848195 4.980000e+09 0.001208 0.086957 0.001814 1.820926e-04 7.010000e+08 4.580000e+08 0.458609 0.601453 1
1 1 0.464291 0.998946 0.848088 6.110000e+09 0.004039 0.064468 0.001286 9.360000e+09 1.065198e-04 2.490000e+09 0.459001 0.610237 1
2 1 0.426071 0.998857 0.848094 7.280000e+09 0.005348 0.014993 0.001495 6.500000e+07 1.791094e-03 7.610000e+08 0.459254 0.601449 1
In [ ]:
prueba2.plot(kind='box')    # vamos a limpiar los atipicos sin afectar materialmente (no quitar de más) el contenido de los datos en este dataset
sns.set(rc={'figure.figsize':(7,7)})
plt.show()
In [ ]:
datos = datos.rename(columns={"ROA(C) before interest and depreciation before interest":"ROA_C", 'Operating Profit Rate':'Operating_Profit_Rate', 'Total Asset Growth Rate':'Total_Asset_Growth_Rate','Current Asset Turnover Rate':'Current_Asset_Turnover_Rate', 'Cash Flow to Liability':'Cash_Flow_to_Liability', 'Operating Profit Growth Rate':'Operating_Profit_Growth_Rate','Accounts Receivable Turnover':'Accounts_Receivable_Turnover','Quick Ratio':'Quick_Ratio'})
In [ ]:
datos.columns
Out[ ]:
Index(['Bankrupt?', 'ROA_C', 'ROA(A) before interest and % after tax',
       'ROA(B) before interest and depreciation after tax',
       'Operating Gross Margin', 'Realized Sales Gross Margin',
       'Operating_Profit_Rate', 'Pre-tax net Interest Rate',
       'After-tax net Interest Rate',
       'Non-industry income and expenditure/revenue',
       'Continuous interest rate (after tax)', 'Operating Expense Rate',
       'Research and development expense rate', 'Cash flow rate',
       'Interest-bearing debt interest rate', 'Tax rate (A)',
       'Net Value Per Share (B)', 'Net Value Per Share (A)',
       'Net Value Per Share (C)', 'Persistent EPS in the Last Four Seasons',
       'Cash Flow Per Share', 'Revenue Per Share (Yuan ¥)',
       'Operating Profit Per Share (Yuan ¥)',
       'Per Share Net profit before tax (Yuan ¥)',
       'Realized Sales Gross Profit Growth Rate',
       'Operating_Profit_Growth_Rate', 'After-tax Net Profit Growth Rate',
       'Regular Net Profit Growth Rate', 'Continuous Net Profit Growth Rate',
       'Total_Asset_Growth_Rate', 'Net Value Growth Rate',
       'Total Asset Return Growth Rate Ratio', 'Cash Reinvestment %',
       'Current Ratio', 'Quick_Ratio', 'Interest Expense Ratio',
       'Total debt/Total net worth', 'Debt ratio %', 'Net worth/Assets',
       'Long-term fund suitability ratio (A)', 'Borrowing dependency',
       'Contingent liabilities/Net worth', 'Operating profit/Paid-in capital',
       'Net profit before tax/Paid-in capital',
       'Inventory and accounts receivable/Net value', 'Total Asset Turnover',
       'Accounts_Receivable_Turnover', 'Average Collection Days',
       'Inventory Turnover Rate (times)', 'Fixed Assets Turnover Frequency',
       'Net Worth Turnover Rate (times)', 'Revenue per person',
       'Operating profit per person', 'Allocation rate per person',
       'Working Capital to Total Assets', 'Quick Assets/Total Assets',
       'Current Assets/Total Assets', 'Cash/Total Assets',
       'Quick Assets/Current Liability', 'Cash/Current Liability',
       'Current Liability to Assets', 'Operating Funds to Liability',
       'Inventory/Working Capital', 'Inventory/Current Liability',
       'Current Liabilities/Liability', 'Working Capital/Equity',
       'Current Liabilities/Equity', 'Long-term Liability to Current Assets',
       'Retained Earnings to Total Assets', 'Total income/Total expense',
       'Total expense/Assets', 'Current_Asset_Turnover_Rate',
       'Quick Asset Turnover Rate', 'Working capitcal Turnover Rate',
       'Cash Turnover Rate', 'Cash Flow to Sales', 'Fixed Assets to Assets',
       'Current Liability to Liability', 'Current Liability to Equity',
       'Equity to Long-term Liability', 'Cash Flow to Total Assets',
       'Cash_Flow_to_Liability', 'CFO to Assets', 'Cash Flow to Equity',
       'Current Liability to Current Assets', 'Liability-Assets Flag',
       'Net Income to Total Assets', 'Total assets to GNP price',
       'No-credit Interval', 'Gross Profit to Sales',
       'Net Income to Stockholder's Equity', 'Liability to Equity',
       'Degree of Financial Leverage (DFL)',
       'Interest Coverage Ratio (Interest expense to EBIT)', 'Net Income Flag',
       'Equity to Liability'],
      dtype='object')
In [ ]:
prueba2 = datos.iloc[:,[0,1,6,25,29,34,45,46,48,71,74,81,89,94]]   # Vamos a tomar algunas caracteristicas que representan ratios de Liquidez, Solvencia y  Rentabilidad, 
                                                                   # así como el indicador de la income flag
In [ ]:
prueba2.head(3)
Out[ ]:
Bankrupt? ROA_C Operating_Profit_Rate Operating_Profit_Growth_Rate Total_Asset_Growth_Rate Quick_Ratio Total Asset Turnover Accounts_Receivable_Turnover Inventory Turnover Rate (times) Current_Asset_Turnover_Rate Cash Turnover Rate Cash_Flow_to_Liability Gross Profit to Sales Net Income Flag
0 1 0.370594 0.998969 0.848195 4.980000e+09 0.001208 0.086957 0.001814 1.820926e-04 7.010000e+08 4.580000e+08 0.458609 0.601453 1
1 1 0.464291 0.998946 0.848088 6.110000e+09 0.004039 0.064468 0.001286 9.360000e+09 1.065198e-04 2.490000e+09 0.459001 0.610237 1
2 1 0.426071 0.998857 0.848094 7.280000e+09 0.005348 0.014993 0.001495 6.500000e+07 1.791094e-03 7.610000e+08 0.459254 0.601449 1
In [ ]:
prueba2['ROA_C'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()
In [ ]:
quantiles = np.percentile(prueba2['ROA_C'], [25,50,75])
quantiles
Out[ ]:
array([0.47652708, 0.5027056 , 0.53556281])
In [ ]:
# Analizando desde el punto de dispersion de los datos 
median = quantiles[1]
IQR = quantiles[2]-quantiles[0]
sigma = 0.75*IQR
In [ ]:
prueba2 = prueba2.query("(ROA_C > @median - 2*@sigma) & (ROA_C < @median + 2*@sigma)")
In [ ]:
prueba2['ROA_C'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()
In [ ]:
prueba2['Operating_Profit_Rate'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()
In [ ]:
quantiles = np.percentile(prueba2['Operating_Profit_Rate'], [25,50,75])
quantiles
Out[ ]:
array([0.99897383, 0.99902097, 0.99908094])
In [ ]:
# Analizando desde el punto de dispersion de los datos 
median = quantiles[1]
IQR = quantiles[2]-quantiles[0]
sigma = 0.75*IQR
In [ ]:
prueba2 = prueba2.query("(Operating_Profit_Rate > @median - 2*@sigma) & (Operating_Profit_Rate < @median + 2*@sigma)")
In [ ]:
prueba2['Operating_Profit_Rate'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()
In [ ]:
prueba2['Total_Asset_Growth_Rate'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()
In [ ]:
quantiles = np.percentile(prueba2['Total_Asset_Growth_Rate'], [25,50,75])
quantiles
Out[ ]:
array([5.45e+09, 6.51e+09, 7.39e+09])
In [ ]:
# Analizando desde el punto de dispersion de los datos 
median = quantiles[1]
IQR = quantiles[2]-quantiles[0]
sigma = 0.75*IQR
In [ ]:
prueba2 = prueba2.query("(Total_Asset_Growth_Rate> @median - 2*@sigma) & (Total_Asset_Growth_Rate < @median + 2*@sigma)")
In [ ]:
prueba2['Total_Asset_Growth_Rate'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()
In [ ]:
prueba2['Current_Asset_Turnover_Rate'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()
In [ ]:
quantiles = np.percentile(prueba2['Current_Asset_Turnover_Rate'], [25,50,75])
quantiles
Out[ ]:
array([0.00013986, 0.00018676, 0.00039851])
In [ ]:
# Analizando desde el punto de dispersion de los datos 
median = quantiles[1]
IQR = quantiles[2]-quantiles[0]
sigma = 0.75*IQR
In [ ]:
prueba2 = prueba2.query("(Current_Asset_Turnover_Rate> @median - 1*@sigma) & (Current_Asset_Turnover_Rate < @median + 1*@sigma)")
In [ ]:
prueba2['Current_Asset_Turnover_Rate'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Cash_Flow_to_Liability

In [ ]:
prueba2['Cash_Flow_to_Liability'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()
In [ ]:
quantiles = np.percentile(prueba2['Cash_Flow_to_Liability'], [25,50,75])
quantiles
Out[ ]:
array([0.45697726, 0.45971628, 0.46339865])
In [ ]:
# Analizando desde el punto de dispersion de los datos 
median = quantiles[1]
IQR = quantiles[2]-quantiles[0]
sigma = 0.75*IQR
In [ ]:
prueba2 = prueba2.query("(Cash_Flow_to_Liability> @median - 2*@sigma) & (Cash_Flow_to_Liability < @median + 2*@sigma)")
In [ ]:
prueba2['Cash_Flow_to_Liability'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Operating Profit Growth Rate

In [ ]:
prueba2['Operating_Profit_Growth_Rate'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()
In [ ]:
quantiles = np.percentile(prueba2['Operating_Profit_Growth_Rate'], [25,50,75])
quantiles
Out[ ]:
array([0.8479869 , 0.84804088, 0.84812266])
In [ ]:
# Analizando desde el punto de dispersion de los datos 
median = quantiles[1]
IQR = quantiles[2]-quantiles[0]
sigma = 0.75*IQR
In [ ]:
prueba2 = prueba2.query("(Operating_Profit_Growth_Rate > @median - 2*@sigma) & (Operating_Profit_Growth_Rate < @median + 2*@sigma)")
In [ ]:
prueba2['Operating_Profit_Growth_Rate'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Accounts_Receivable_Turnover

In [ ]:
prueba2['Accounts_Receivable_Turnover'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()
In [ ]:
quantiles = np.percentile(prueba2['Accounts_Receivable_Turnover'], [25,50,75])
quantiles
Out[ ]:
array([0.00064521, 0.00081361, 0.00109361])
In [ ]:
# Analizando desde el punto de dispersion de los datos 
median = quantiles[1]
IQR = quantiles[2]-quantiles[0]
sigma = 0.75*IQR
In [ ]:
prueba2 = prueba2.query("(Accounts_Receivable_Turnover > @median - 2*@sigma) & (Accounts_Receivable_Turnover < @median + 2*@sigma)")
In [ ]:
prueba2['Accounts_Receivable_Turnover'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Quick_Ratio

In [ ]:
prueba2['Quick_Ratio'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()
In [ ]:
quantiles = np.percentile(prueba2['Quick_Ratio'], [25,50,75])
quantiles
Out[ ]:
array([0.00501926, 0.00694272, 0.01001482])
In [ ]:
# Analizando desde el punto de dispersion de los datos 
median = quantiles[1]
IQR = quantiles[2]-quantiles[0]
sigma = 0.75*IQR
In [ ]:
prueba2 = prueba2.query("(Quick_Ratio > @median - 3*@sigma) & (Quick_Ratio < @median + 3*@sigma)")
In [ ]:
prueba2['Quick_Ratio'].plot(kind='box')
sns.set(rc={'figure.figsize':(7,7)})
plt.show()

Revision de la aplicación sobre los Atípicos

In [ ]:
prueba2.plot(kind='box')    # vamos a limpiar los atipicos sin afectar materialmente (no quitar de más) el contenido de los datos en este dataset
sns.set(rc={'figure.figsize':(12,12)})
plt.show()
In [ ]:
prueba2.plot(kind='box',subplots=True, layout=(4,4), sharex=False, sharey=False)
sns.set(rc={'figure.figsize':(20,20)})
plt.show()
In [ ]:
X = prueba2  # Renombrando variable para utilizarla en Scikit-Learn
In [ ]:
# Normalizando dataframe
scaler = StandardScaler()
X_std = scaler.fit_transform(X)
In [ ]:
# Importando PCA

pca = PCA()
pca.fit(X_std)
Out[ ]:
PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,
    svd_solver='auto', tol=0.0, whiten=False)
In [ ]:
# El atributo muestra cuanta varianza es explicada por cada uno de las 13 variables
evr = pca.explained_variance_ratio_
evr
Out[ ]:
array([0.1998169 , 0.14067149, 0.09706887, 0.08421156, 0.07719616,
       0.07622867, 0.06969375, 0.06914001, 0.05593517, 0.04734506,
       0.03892548, 0.02805176, 0.01571513, 0.        ])
In [ ]:
# Ploteando grafico de Componentes principales
fig = plt.figure(figsize=(8,8))
plt.plot(range(1, len(X.columns)+1), evr.cumsum(), marker='o', linestyle=':')
plt.xlabel('Numero de Componentes', fontsize=18)
plt.ylabel('Varianza Acumulada Explicada',fontsize=18)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.show()
In [ ]:
# Iteracion para comprobar numero de componentes optimos a utilizar por su nivel de varianza

for i, exp_var in enumerate(evr.cumsum()):
    if exp_var >= 0.8:
        n_comps = i + 1
        break
print("Numero de Componentes Optimos:", n_comps)
pca = PCA(n_components=n_comps)
pca.fit(X_std)
scores_pca = pca.transform(X_std)
Numero de Componentes Optimos: 8

Algoritmo k-means

In [ ]:
# Encontrando el punto del codo de la curva de WCSS (dentro de la suma de cuadrados) usando el KneedLocator
wcss = []
max_clusters = 21
for i in range(1, max_clusters):
    kmeans_pca = KMeans(i, init='k-means++', random_state=42)
    kmeans_pca.fit(scores_pca)
    wcss.append(kmeans_pca.inertia_)
n_clusters = KneeLocator([i for i in range(1, max_clusters)], wcss, curve='convex', direction='decreasing').knee
print("Numero de Clusters Optimos:", n_clusters)
Numero de Clusters Optimos: 6
In [ ]:
# Ploteando grafico 
fig = plt.figure(figsize=(8,8))
plt.plot(range(1, 21), wcss, marker='o', linestyle=':')
plt.vlines(KneeLocator([i for i in range(1, max_clusters)], wcss, curve='convex', 
                       direction='decreasing').knee, ymin=min(wcss), ymax=max(wcss), linestyles='dashed')
plt.xlabel('Numero de Clusters', fontsize=18)
plt.ylabel('Dentro del cluster [Suma de cuadrados] (WCSS)', fontsize=18)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.show()
In [ ]:
# Creando la optimizacion de parametros con PCA y K-Means
kmeans_pca = KMeans(n_clusters=n_clusters, init='k-means++', random_state=42)
kmeans_pca.fit(scores_pca);

Analisis y Visualizacion

In [ ]:
# Etiquentando cada uno de los datos dentro del cluster respectivo
df_seg_pca_kmeans = pd.concat([pd.DataFrame(X.reset_index(drop=True)), pd.DataFrame(scores_pca)], axis=1)
df_seg_pca_kmeans.columns.values[(-1*n_comps):] = ["Component " + str(i+1) for i in range(n_comps)]
df_seg_pca_kmeans['Cluster'] = kmeans_pca.labels_
df_seg_pca_kmeans.head()
Out[ ]:
Bankrupt? ROA_C Operating_Profit_Rate Operating_Profit_Growth_Rate Total_Asset_Growth_Rate Quick_Ratio Total Asset Turnover Accounts_Receivable_Turnover Inventory Turnover Rate (times) Current_Asset_Turnover_Rate Cash Turnover Rate Cash_Flow_to_Liability Gross Profit to Sales Net Income Flag Component 1 Component 2 Component 3 Component 4 Component 5 Component 6 Component 7 Component 8 Cluster
0 1 0.464291 0.998946 0.848088 6.110000e+09 0.004039 0.064468 0.001286 9.360000e+09 0.000107 2.490000e+09 0.459001 0.610237 1 -2.604889 0.776264 -2.925775 4.086321 -1.844398 0.414805 4.488900 3.557385 2
1 0 0.488519 0.998961 0.848159 6.890000e+09 0.011823 0.154423 0.001303 3.041883e-04 0.000103 1.052297e-04 0.462165 0.603613 1 0.016972 -2.065863 0.289338 0.917354 0.039184 1.403255 1.147297 -1.325033 0
2 0 0.444401 0.998975 0.847937 5.730000e+09 0.005123 0.062969 0.000808 6.550000e+08 0.000128 3.370000e+08 0.459710 0.623709 1 -1.674525 1.038563 0.420019 -0.644458 -0.733692 1.202720 0.989238 -0.078540 1
3 0 0.535953 0.999119 0.848080 6.960000e+09 0.007383 0.125937 0.001234 6.900000e+09 0.000118 1.240000e+09 0.458303 0.618452 1 2.070260 0.048510 -1.588057 -0.438752 -0.807130 1.758889 0.624107 0.114445 4
4 0 0.487398 0.999042 0.847986 6.250000e+09 0.008200 0.118441 0.001165 4.030000e+07 0.000123 8.490000e+09 0.462332 0.636256 1 1.157238 0.161093 -0.423278 -1.748952 0.197452 0.362270 2.730395 0.890274 4
In [ ]:
# Creando visualizacion de los datos con PCA

x = df_seg_pca_kmeans['Component 2']
y = df_seg_pca_kmeans['Component 1']
fig = plt.figure(figsize=(10, 8))
sns.scatterplot(x, y, hue=df_seg_pca_kmeans['Cluster'], palette = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:pink'])
plt.title('Clusters vistos con PCA', fontsize=20)
plt.xlabel("Componente 2", fontsize=18)
plt.ylabel("Componente 1", fontsize=18)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.show();

Se observa mas disperso los cluster en esta prueba realizada posteriormente a la limpieza de los valores atipicios

Conclusiones

Por medio del uso de modelos de Machine Learning Se pueden evaluar las variables que influyen en la determinación o no de la posibilidad que una empresa presente bancarrota. Estas posibilidades que se aplican a la determinación de las posibilidades financieras de una empresa, pueden aplicarse a individuos en procesos de clasificación o aprobación de créditos.

Resumen

Se recibió un dataset con información financiera que ayduda al proceso de análisis crediticio, apra determinar si las empresas podrían o no caer en bancarota.

Por lo que en el proceso de este análisis formuláos las siguientes preguntas:

Qué se requiere analizar en una empresa para determinar la salud o liquidez financiera?

Según la información brindada se puede inferir si una empresa puede o no caer en bancarrota?

In [ ]:
data1.describe()
Out[ ]:
Bankrupt? ROA(C) before interest and depreciation before interest ROA(A) before interest and % after tax ROA(B) before interest and depreciation after tax Operating Gross Margin Realized Sales Gross Margin Operating Profit Rate Pre-tax net Interest Rate After-tax net Interest Rate Non-industry income and expenditure/revenue Continuous interest rate (after tax) Operating Expense Rate Research and development expense rate Cash flow rate Interest-bearing debt interest rate Tax rate (A) Net Value Per Share (B) Net Value Per Share (A) Net Value Per Share (C) Persistent EPS in the Last Four Seasons Cash Flow Per Share Revenue Per Share (Yuan ¥) Operating Profit Per Share (Yuan ¥) Per Share Net profit before tax (Yuan ¥) Realized Sales Gross Profit Growth Rate Operating Profit Growth Rate After-tax Net Profit Growth Rate Regular Net Profit Growth Rate Continuous Net Profit Growth Rate Total Asset Growth Rate Net Value Growth Rate Total Asset Return Growth Rate Ratio Cash Reinvestment % Current Ratio Quick Ratio Interest Expense Ratio Total debt/Total net worth Debt ratio % Net worth/Assets Long-term fund suitability ratio (A) ... Current Assets/Total Assets Cash/Total Assets Quick Assets/Current Liability Cash/Current Liability Current Liability to Assets Operating Funds to Liability Inventory/Working Capital Inventory/Current Liability Current Liabilities/Liability Working Capital/Equity Current Liabilities/Equity Long-term Liability to Current Assets Retained Earnings to Total Assets Total income/Total expense Total expense/Assets Current Asset Turnover Rate Quick Asset Turnover Rate Working capitcal Turnover Rate Cash Turnover Rate Cash Flow to Sales Fixed Assets to Assets Current Liability to Liability Current Liability to Equity Equity to Long-term Liability Cash Flow to Total Assets Cash Flow to Liability CFO to Assets Cash Flow to Equity Current Liability to Current Assets Liability-Assets Flag Net Income to Total Assets Total assets to GNP price No-credit Interval Gross Profit to Sales Net Income to Stockholder's Equity Liability to Equity Degree of Financial Leverage (DFL) Interest Coverage Ratio (Interest expense to EBIT) Net Income Flag Equity to Liability
count 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6.819000e+03 6.819000e+03 6819.000000 6.819000e+03 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6.819000e+03 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6.819000e+03 6.819000e+03 6819.000000 6819.000000 6.819000e+03 6.819000e+03 6819.000000 6.819000e+03 6819.000000 6819.000000 6819.000000 ... 6819.000000 6819.000000 6.819000e+03 6.819000e+03 6819.000000 6819.000000 6819.000000 6.819000e+03 6819.000000 6819.000000 6819.000000 6.819000e+03 6819.000000 6819.000000 6819.000000 6.819000e+03 6.819000e+03 6819.000000 6.819000e+03 6819.000000 6.819000e+03 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6.819000e+03 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.000000 6819.0 6819.000000
mean 0.032263 0.505180 0.558625 0.553589 0.607948 0.607929 0.998755 0.797190 0.809084 0.303623 0.781381 1.995347e+09 1.950427e+09 0.467431 1.644801e+07 0.115001 0.190661 0.190633 0.190672 0.228813 0.323482 1.328641e+06 0.109091 0.184361 0.022408 0.847980 0.689146 0.689150 0.217639 5.508097e+09 1.566212e+06 0.264248 0.379677 4.032850e+05 8.376595e+06 0.630991 4.416337e+06 0.113177 0.886823 0.008783 ... 0.522273 0.124095 3.592902e+06 3.715999e+07 0.090673 0.353828 0.277395 5.580680e+07 0.761599 0.735817 0.331410 5.416004e+07 0.934733 0.002549 0.029184 1.195856e+09 2.163735e+09 0.594006 2.471977e+09 0.671531 1.220121e+06 0.761599 0.331410 0.115645 0.649731 0.461849 0.593415 0.315582 0.031506 0.001173 0.807760 1.862942e+07 0.623915 0.607946 0.840402 0.280365 0.027541 0.565358 1.0 0.047578
std 0.176710 0.060686 0.065620 0.061595 0.016934 0.016916 0.013010 0.012869 0.013601 0.011163 0.012679 3.237684e+09 2.598292e+09 0.017036 1.082750e+08 0.138667 0.033390 0.033474 0.033480 0.033263 0.017611 5.170709e+07 0.027942 0.033180 0.012079 0.010752 0.013853 0.013910 0.010063 2.897718e+09 1.141594e+08 0.009634 0.020737 3.330216e+07 2.446847e+08 0.011238 1.684069e+08 0.053920 0.053920 0.028153 ... 0.218112 0.139251 1.716209e+08 5.103509e+08 0.050290 0.035147 0.010469 5.820516e+08 0.206677 0.011678 0.013488 5.702706e+08 0.025564 0.012093 0.027149 2.821161e+09 3.374944e+09 0.008959 2.938623e+09 0.009341 1.007542e+08 0.206677 0.013488 0.019529 0.047372 0.029943 0.058561 0.012961 0.030845 0.034234 0.040332 3.764501e+08 0.012290 0.016934 0.014523 0.014463 0.015668 0.013214 0.0 0.050014
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000e+00 0.000000e+00 0.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000e+00 0.000000e+00 0.000000 0.000000 0.000000e+00 0.000000e+00 0.000000 0.000000e+00 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000e+00 0.000000e+00 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000e+00 0.000000e+00 0.000000 0.000000e+00 0.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.0 0.000000
25% 0.000000 0.476527 0.535543 0.527277 0.600445 0.600434 0.998969 0.797386 0.809312 0.303466 0.781567 1.566874e-04 1.281880e-04 0.461558 2.030203e-04 0.000000 0.173613 0.173613 0.173676 0.214711 0.317748 1.563138e-02 0.096083 0.170370 0.022065 0.847984 0.689270 0.689270 0.217580 4.860000e+09 4.409689e-04 0.263759 0.374749 7.555047e-03 4.725903e-03 0.630612 3.007049e-03 0.072891 0.851196 0.005244 ... 0.352845 0.033543 5.239776e-03 1.973008e-03 0.053301 0.341023 0.277034 3.163148e-03 0.626981 0.733612 0.328096 0.000000e+00 0.931097 0.002236 0.014567 1.456236e-04 1.417149e-04 0.593934 2.735337e-04 0.671565 8.536037e-02 0.626981 0.328096 0.110933 0.633265 0.457116 0.565987 0.312995 0.018034 0.000000 0.796750 9.036205e-04 0.623636 0.600443 0.840115 0.276944 0.026791 0.565158 1.0 0.024477
50% 0.000000 0.502706 0.559802 0.552278 0.605997 0.605976 0.999022 0.797464 0.809375 0.303525 0.781635 2.777589e-04 5.090000e+08 0.465080 3.210321e-04 0.073489 0.184400 0.184400 0.184400 0.224544 0.322487 2.737571e-02 0.104226 0.179709 0.022102 0.848044 0.689439 0.689439 0.217598 6.400000e+09 4.619555e-04 0.264050 0.380425 1.058717e-02 7.412472e-03 0.630698 5.546284e-03 0.111407 0.888593 0.005665 ... 0.514830 0.074887 7.908898e-03 4.903886e-03 0.082705 0.348597 0.277178 6.497335e-03 0.806881 0.736013 0.329685 1.974619e-03 0.937672 0.002336 0.022674 1.987816e-04 2.247728e-04 0.593963 1.080000e+09 0.671574 1.968810e-01 0.806881 0.329685 0.112340 0.645366 0.459750 0.593266 0.314953 0.027597 0.000000 0.810619 2.085213e-03 0.623879 0.605998 0.841179 0.278778 0.026808 0.565252 1.0 0.033798
75% 0.000000 0.535563 0.589157 0.584105 0.613914 0.613842 0.999095 0.797579 0.809469 0.303585 0.781735 4.145000e+09 3.450000e+09 0.471004 5.325533e-04 0.205841 0.199570 0.199570 0.199612 0.238820 0.328623 4.635722e-02 0.116155 0.193493 0.022153 0.848123 0.689647 0.689647 0.217622 7.390000e+09 4.993621e-04 0.264388 0.386731 1.626953e-02 1.224911e-02 0.631125 9.273293e-03 0.148804 0.927109 0.006847 ... 0.689051 0.161073 1.295091e-02 1.280557e-02 0.119523 0.360915 0.277429 1.114677e-02 0.942027 0.738560 0.332322 9.005946e-03 0.944811 0.002492 0.035930 4.525945e-04 4.900000e+09 0.594002 4.510000e+09 0.671587 3.722000e-01 0.942027 0.332322 0.117106 0.663062 0.464236 0.624769 0.317707 0.038375 0.000000 0.826455 5.269777e-03 0.624168 0.613913 0.842357 0.281449 0.026913 0.565725 1.0 0.052838
max 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 9.990000e+09 9.980000e+09 1.000000 9.900000e+08 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 3.020000e+09 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 9.990000e+09 9.330000e+09 1.000000 1.000000 2.750000e+09 9.230000e+09 1.000000 9.940000e+09 1.000000 1.000000 1.000000 ... 1.000000 1.000000 8.820000e+09 9.650000e+09 1.000000 1.000000 1.000000 9.910000e+09 1.000000 1.000000 1.000000 9.540000e+09 1.000000 1.000000 1.000000 1.000000e+10 1.000000e+10 1.000000 1.000000e+10 1.000000 8.320000e+09 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 9.820000e+09 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.0 1.000000

8 rows × 96 columns

In [1346]:
# Detalle de los tipos de Ratios Financieros que se utilizan en Evaluaciones Financieras
from PIL import Image
Image.open('/content/drive/MyDrive/Data Sets/Types-of-Ratio.png')
Out[1346]:

Los tres elementos determinantes de todo análisis financiero son:

Liquidez: capacidad para hacer frente y cumplir con sus obligaciones financieras a corto plazo.

Solvencia: como responder a los compromisos de largo plazo (endeudamiento).

Rentabilidad: capacidad de generar ingresos/beneficios, se puede medir el nivel de eficiencia con el que los recursos son utilizados en la empresa.

Según se indica en Análisis y Dianóstico Financiero Enfoque Integral. Tarcisio Salas (2012). El análisis financiero permite evaluar el cumplimiento de las metas y planes y el desempeño de la empresa en las áreas claves de la administración. La obtención y utilización de fondos se reflejan en el balance general. Los ingresos, costos, gastos, y ganancias, derivados del manejo de fondos en las operaciones, se concretan en el estado de resultados. El análisis financiero examina las relaciones entre datos de ambos estados, con la finalidad de calificar la gestión y el grado de éxito alcanzado por la empresa. Los tópicos más importantes cubiertos por el análisis son los siguientes:

  • Posición de liquidez y flujo de efectivo
  • Nivel y efecto del endeudamiento (apalancamiento financiero)
  • Financamiento y estructura de capital
  • Rendimiento de inversiones de capital
  • Manejo y eficiencia de inversiones en activo circulante
  • Márgenes de utilidad y estructura de costos y gastos
  • Rentabilidad del patrimonio

Referencias Primarias

Uso de código visto en la web

PETR KOLAR https://www.kaggle.com/petrkolar/ml-workflow-0-99-f1

MULTICOLLINEARITY (CORRELATION BETWEEN PREDICTOR VARIABLES)

cor_matrix = df.corr().abs() cor_matrix.style.background_gradient(sns.light_palette('red', as_cmap=True))

Referencias Secundarias

Para referencia de información (https://isslab.csie.ncu.edu.tw/download/publications/1.pdf) Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study

Measuring the Defensive Position of a Firm, Sidney Davidson, George H. Sorter and Hemu Kalle

https://www.jstor.org/stable/4469589